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About this course
Over the past decade emerging technologies, paired with massive changes in regulations, have driven an unprecedented transformation of finance around the world. This process is happening more rapidly in China and Asia than anywhere else. This course is designed to explore FinTech fundamentals and help make sense of this wave of change as it happens.
New players such as start-ups and technology firms are challenging traditional players in finance, bringing democratization, inclusion and disruption. Companies engaged in social media, e-commerce, and telecommunications, as well as, companies and start-ups with large customer data pools, creative energies, and technical capacities, have brought competition to the existing financial infrastructure and are remaking the industry.
These transformations have not only created challenges but also unprecedented opportunities, building synergies with new business and regulatory models, particularly in emerging markets and developing countries. To meet these changes, 21st-century professionals and students must be equipped with up-to-date knowledge of the industry and its incredible evolution. This course – designed by HKU with the support of SuperCharger and the Centre for Finance, Technology and Education – is designed to enable learners with the necessary tools to understand the complex interaction of finance, technology and regulation.
In this course, through a series of video lectures, case studies, and assessments you will explore the major areas of FinTech including, beginning with What is FinTech before turning to Money, Payment and Emerging Technologies, Digital Finance and Alternative Finance, FinTech Regulation and RegTech, Data and Security, and the Future of Data Driven Finance, as well as, the core technologies driving FinTech including Blockchain, AI and Big Data. These will set the stage for understanding the FinTech landscape and ecosystem and grappling with the potential direction of future change.
FinTech Evolution 3.0 & 3.5: Startups and Emerging Markets
Industry Showcase: Collaboration between Financial Institutions and Startups
Emerging Economics: Opportunities and Challenges
From too-Small-To-Care to Too-Big-To-Fail
Introduction to Regulation
Industry Showcase: The Future of RegTech and 6 Technologies Impacting It
Module 2 Payments, Cryptocurrencies and Blockchain
Developing Countries and DFS: The Story of Mobile Money
Developing Countries and DFS: Regulation of Mobile Money
The ABCDs of Alternative Finance
Building a New stack
Industry Showcase : Legal and Regulatory Implications of Cryptocurrencies
What is Blockchain?
Industry Showcase: The Benefits from New Payment Stacks (Applications of Ripple)
Module 3 Digital Finance and Alternative Finance
A Brief History of Financial Innovation
Digitization of Financial Services
FinTech & Funds
Industry Showcase: How AI is Transforming the Future of FinTech
Industry Showcase: Ensuring Compliance from the Start: Suitability and Funds
Crowdfunding – Regards, Charity and Equity
P2P and Marketplace Lending
The Rise of Chinese TechFins – New Models and New Products
What is an ICO?
Module 4 FinTech Regulation and RegTech
Evolution of RegTech
RegTech Ecosystem: Financial Institutions
RegTech Ecosystem: Startups
RegTech Startups: Challenges
RegTech Ecosystem: Regulators
Industry Showcase: Use Case of AI in Smart Regulation and Fraud Detection
Redesigning Better Financial Infrastructure
Module 5 Data & TechFin
History of Data Regulation
Data in Financial Services
Industry Showcase : Application of Data Analytics in Finance
European Big-Bang: PSD2 / GDPR / Mifid2
Industry Showcase : PSD2: Open Banking API Will Help Startups
Industry Showcase : Methods of Data Protection: GDPR Compliance and Personal Privacy
Change in mindset: Regulation 1.0 to 2.0 (KYC to KYD)
AI & Governance
New Challenges of AI and Machine Learning
Data, Metadata and Differential Privacy
Data is the New Oil: Risk of Breach
Industry Showcase : Cybersecurity Industry Update
Module 6 The Future of Data-Driven Finance
Case Study 1: Revolut
Case Study 2: Alibaba
Case Study 3: Aadhaar
Case Study 4: Credit Karma
Case Study 5: Digibank
Conclusion to Case Studies
FinTech Big Trends – Looking Forward
Welcome and Course Administration
Welcome to Introduction of FinTech
FinTech involves the use of technology,
particularly information technology
to transform the way that finance is being done
in global markets, developing countries,
and across start-ups and tech firms.
20 years ago, IBM spent $100 million
to build Deep Blue, the super computer
that beat Garry Kasparov.
This smartphone is more powerful than Deep Blue.
If you want to be in finance today,
you need to understand technology.
RegTech, short for regulatory technology
may be a game changer
as it will allow financial institutions
to deal with compliance and regulatory burdens,
not only more effectively,
but also more efficiently.
FinTech flourishes where the need is greatest.
China leads the world in many regards.
YueBao is a money market fund enabled by FinTech in China.
In 9 months, it became the fourth largest in the world.
Now 3 years later, it is the world’s largest.
FinTech has evolved over 3 eras,
from infrastructure to banks to start-ups,
entrepreneurs today are building the B2B solution
that will be powering the financial system tomorrow.
This course will be an illustration
of how this is happening today.
In the future, finance will be
about an experience, not a product.
That’s why it’s important for students today
to understand how the industry is changing
and that’s what this course will be about.
Course Outline and Syllabus
Introduction to FinTech Course – Course Outline
Introduction to FinTech is a six-week, six-module course. Each weekly module compiles 8-12 lesson units (or subsections). In addition to the main units of the lesson, there are also Industry Showcases highlighting examples and experiences. These include segments from the traditional financial services industry, startups, technology firms, and more.
The major learning activities within each lesson unit include: video discussions of major aspects, as well as continuous assessments in the form of Quick Check questions, Polling, and Word Cloud activities. In addition, there are a range of additional resources, including reports, studies, and useful links. There is a Conclusion Quiz at the end of each module to draw out the main messages.
FinTech Evolution 3.0 & 3.5: Startups and Emerging Markets
Collaboration between Financial Institutions and Startups (The FinTech Association of Hong Kong)
Emerging Economics: Opportunities and Challenges
From Too-Small-To-Care to Too-Big-To-Fail
Introduction to Regulation
The Future of RegTech and Six Technologies Impacting It (Thomson Reuters)
Module 2: Payment, Cryptocurrencies and Blockchain
Module 2 Introduction
Developing Countries and DFS: The Story of Mobile Money
Developing Countries and DFS: Regulation of Mobile Money
The ABCDs of Alternative Finance (Parts 1 & 2)
Building a New Stack
Introduction to Digital Asset Market (Gatecoin)
Stablecoins (Feron Labs)
Legal and Regulatory Implications of Cryptocurrencies (King & Wood Mallesons)
What is Blockchain?
The Benefits from New Payment Stacks (Applications of Ripple for Standard Chartered Bank)
Module 3: Digital Finance and Alternative Finance
Module 3 Introduction
A Brief History of Financial Innovation
Digitization of Financial Services
FinTech & Funds
How AI is Transforming the Future of FinTech (Microsoft)
How Will AI Transform Financial Analysis? (MioTech)
Ensuring Compliance from the Start: Suitability and Funds (Investment Navigator)
Crowdfunding – Regards, Charity and Equity
P2P and Marketplace Lending
The Rise of Chinese TechFins – New Models and New Products
Collaborative and Contextual Banking (WeBank)
Module 4: FinTech Regulation and RegTech
Module 4 Introduction
FinTech Regulations (Parts 1 & 2)
Evolution of RegTech
RegTech Ecosystem: Financial Institutions
RegTech Ecosystem: Startups
RegTech Startups: Challenges
RegTech Ecosystem: Regulators
The Application of AI in Smart Regulation (Mindbridge)
Balancing Innovation and Regulation Challenges in Hong Kong (Charles Mok)
Redesigning Better Financial Infrastructure: India Stack
Module 5: Data and TechFin
Module 5 Introduction
History of Data Regulation
Data in Financial Services
Application of Data Analytics in Finance (vPhrase)
European Big-Bang: PSD2 / GDPR / MiFID2
PSD2: Open Banking API for Startups (Gini)
Methods of Data Protection: GDPR Compliance and Personal Privacy (Exate Technology)
Change in Mindset: Regulation 1.0 to 2.0 (KYC to KYD)
AI and Governance
New Challenges of AI and Machine Learning
Data, Metadata and Differential Privacy
Data is the New Oil: Risk of Breach
Cybersecurity Industry Update (Microsoft)
Module 6: The Future of Data-Driven Finance
Module 6 Introduction
Case Study 1: Revolut
Case Study 2: Alibaba
Case Study 3: Aadhaar
Case Study 4: Credit Karma
Case Study 5: Digibank
Conclusion to Case Studies
FinTech Big Trends – Looking Forward
The Next Big Opportunities in FinTech (Charles Mok)
The FinTech Landscape in China – What’s Next? (Charles Mok)
Research and Development (R&D) and Interactions with Industries (Charles Mok)
Module 1 What is FinTech?
Welcome to Module 1
1.1 Module 1 Introduction
Welcome, my name is Douglas Arner
and this is an introduction to FinTech.
I am the Kerry Holdings Professor in Law
at the University of Hong Kong
and I have spent the past 25 years
studying the interaction
between finance, technology and regulation.
And in this course,
we are going to provide you
an introduction to the world of FinTech.
Financial technology transforming
the world of finance
and the wider world beyond
faster than we have ever seen before.
In this course,
I am joined with a group of my friends
from around the world,
Ross Buckley of the University of New South Wales in Australia,
Henri Arslanian of PwC,
Brian Tang of the Asia Capital Markets Institute,
Janos Barberis of SuperCharger
and Huy Nguyen Trieu of the Centre for Finance, Technology and Entrepreneurship.
In this course, we’re going to begin
by looking at the fundamental question.
What is FinTech?
And that is going to be module 1.
For module 2,
we’ll turn to the next question
which is money, payment
and the transformation of finance through technology,
looking at new things
like cryptocurrencies, Bitcoin, blockchain
as well as, mobile payments.
In module 3,
we’ll turn to the digitization of finance
and the development of new forms
of alternative finance, crowdfunding, ICOs,
new forms of lending and security settlement.
In module 4, we’ll turn to
some of the bigger challenges,
How do we balance the opportunities
and risks of FinTech
so that we can make not only FinTech better
but the financial system
and the wider economy?
In module 5, we’ll turn to
one of the biggest challenges
facing the world of finance today
and that is the interaction
between data and finance
and the emergence of an entirely new paradigm
of data-driven finance
which brings with it tremendous changes
but also tremendous risks.
And finally, in the last module,
module 6, we’ll look at
a number of case studies
to draw lessons from our experiences
and also look at
some of the big trends going forward.
So, we very much look forward to you joining this
in our journey across FinTech.
At the end of this course,
we very much hope you will have
an overall understanding of what is FinTech,
of some of the major technologies
that are driving FinTech transformation,
an understanding of what is happening
not only in developing markets
but in particular,
in exciting new emerging markets,
particularly in Asia.
And finally, that you will be able to
understand in your own life,
in your own career
how technology could continue
to transform finance in the future.
Module 1 Learning Objectives
Module 1 introduces the evolution, context, opportunities, and challenges of FinTech. It highlights to students that FinTech should be defined by the user of technology as opposed to simply the fact of using technology (the ‘who’, not the ‘what’) so as to better understand why this industry is growing ever more rapidly and what opportunities and risks it creates.
Key Learning Objectives:
Understand how finance and technology have evolved and are transforming finance around the world.
Discuss key interactions between finance and technology over time to better understand the developments which are taking place today and are likely to take place in the future.
Consider the broad spectrum of the financial sector and the way that technology is changing it faster than ever before, particularly with the explosion of new entrants, including startups, tech firms and emerging markets.
Consider both the opportunities as well as the potential risks of FinTech and the challenges it poses for policymakers.
1.2 FinTech Transformation
Hi there, very excited to be here.
My name is Henri Aslanian
and my passion and focus is the future
of the financial services industry.
And I’m very lucky to be able to
do this in my academic life
as an Adjunct Associate Professor
here at the University of Hong Kong,
where I teach the first
FinTech university course in Asia.
Also very lucky to be
doing this in my professional life
as the FinTech and RegTech leader
here for Hong Kong for PwC.
Very excited to be sharing about
the latest developments
going on in the broader FinTech space
throughout this course.
One question often pops up is that
how come FinTech became such a big reality?
What really caused the rise of FinTech?
Well it’s a very interesting question.
Traditionally… actually, as technology evolved
banks were pretty good at also
always keeping up with technology
and in many cases,
being some of the early adopters.
But all of this really changed
during the global financial crisis in 2008.
During that time,
banks and financial institutions
were busy dealing with regulations, compliance,
and other many regulatory enforcement situations
that were taking place.
Innovation became a very, very distant priority.
But at the same time,
some of the biggest game changing innovations
that took place
that have changed our lives took place.
Think about Uber, WhatsApp,
WeChat, or Airbnb,
and many others that really changed the way
customers ahead experience the services
they were receiving in many industries,
except financial services,
and this gap was created
between what financial institutions
were offering to their customers
and what customers came to expect.
And this gap is
what the FinTech industry wanted to tackle.
And the FinTech industry wanted to tackle
very many of the use cases;
that matched the pain points
that were in this industry.
However, it was not only the startups.
And actually what we realised
over the course of the years is actually
some of the biggest gamechanging
technological innovations took place
not only by FinTech startups
but by large technology firms.
Think about firms like
Amazon, or Tencent, or Ant Financial
and many others who have started now
looking at financial services.
And in the future
there’s actually a big chance that
some of the providers of financial services,
at least the interface
that clients will be using
may be these large technology firms.
So definitely an area to watch.
And what are some of the advantages
these large technology firms have
over some of these startups?
Not only do they have the funding,
they have the backing,
and the talent, and the pool, capital pool
to actually support these new businesses,
but to certain extent,
they have the trust of consumers.
Think about it,
if you’re happy to buy
all your daily necessities
on Amazon, or on Taobao,
wouldn’t you use them as well
to actually, you know,
buy insurance products?
If you’re actually using the Facebook Messenger
to actually talk to your friends and family
wouldn’t you use them as well to send money
to friends and family as well?
Well, as you can see,
it’s going to be a very interesting ecosystem
developing over the coming years
not only with FinTech,
but also by TechFin.
Definitely an area to watch.
1.3 FinTech Evolution 1.0: Infrastructure
Finance and technology
have been inextricably intertwined
since the very beginning.
If we look at the earliest days of finance
dating back thousands of years ago,
the initial impact of technology
was in the context of building systems
of keeping records of government finances
or payments for taxes
and agricultural production
or building facilities
and one of the first physical technologies
to develop was money
the simple coin
or thousands of years later,
not long after 1000 A.D.,
Money is a form of technology
that allows us to physically handle
the ideas embedded in finance.
So, if we think about financial technology,
the history is very, very long indeed
and in addition to coins,
paper money, systems of writing
major points include the evolution
of the joint stock company
or the corporation,
forms of financing like banking,
or more recently, stocks, bonds,
things that we would call
securities or derivatives.
Derivatives are financial instruments
whose value derives
from some underlying financial instrument
but if we think of today’s fintech,
the modern period of fintech
and financial technology
begins around 150 years ago
and we generally mark the date as 1867.
1867 was the date of the establishment
of the first transatlantic telegraph cable.
That transatlantic telegraph cable
made instantaneous communication
between the major markets
of New York and London,
or London and Paris,
or even eventually several decades later,
Shanghai, or Hong Hong, and London possible.
This was the basic infrastructure
that underlies all of today’s
not only financial technology
but much of today’s communications
and media developments as well
and it’s somewhat ironic
that in the past five years
more undersea cable has been laid
than in the entire previous 150 years combined.
Because more physical cables are necessary
to handle the ever greater flows of data
speeding around the world
the development of finance.
Many would say
that the period from the 1860s
up to the start of the First World War
was a first period of
financial and economic globalisation
much like the past 30 years or so from today.
That period of
financial and economic globalisation
was underpinned by technological infrastructure
like the transatlantic telegraph cable
which in fact has been called
the Victorian internet.
Now, if we think about
in financial services,
probably our next significant milestone
really comes during the Second War World.
During the Second World War,
significant effort spent particularly
in the context of developing codes
for secure communications
particularly of military and intelligence operations
as well as, significant efforts
to develop systems to break those codes
and it was this process of encoding
and breaking codes that led
to come of the groundbreaking thinking
in computer technology
and in fact,
eventually in artificial intelligence or AI
which is one of the most exciting developments
happening in financial technology today.
But it wasn’t until the period
after the Second War World
as the world economy is rebuilding
that we begin to see progressive development
of those early computer technologies
to eventually lay the ground work
for the sorts of fintech
and regtech developments
which we see today.
1.4 FinTech Evolution 2.0: Banks
The first era of modern fintech,
fintech 1.0 was about
building the underlying infrastructure
that supports today’s global financial markets.
The second major era
of the modern fintech evolution,
what we call fintech 2.0,
started in 1967.
1967 marks two very important dates
in the evolution of finance
and also technology.
The first, is the establishment of the first ATM,
the first automated teller machine
by Barclays Bank in the UK.
That automated teller machine allowed,
over the next several decades,
a transformation in the relationship
that people had with money and with finance.
The second, just as significant,
is the launch of the first handheld calculator
by Texas Instruments.
The first handheld calculator
was transformational in the way
that finance, on a day-to-day basis operated
And, of course, the handheld calculator
is also the ancestor of today’s smartphone,
perhaps the transformative technology
in the context of fintech.
1967 thus marks a period where we begin
to see a process of digitization.
Digitization is taking processes and systems,
which were formerly analogue,
analogue things like handwriting
or physical calculation of money,
and digitising them, transforming them
into a digital environment.
And from these early beginnings,
we can see in global financial markets,
three very significant trends
that come together
in today’s global financial markets.
The first, the late 1960s and early 1970s,
the establishment of a series of
domestic and international
electronic payment systems.
These payment systems allow
large value payments
to take place today on a real time basis,
which underpins massive volumes
of transactions around the world.
From across border standpoint,
perhaps the most significant of these
is an organisation,
which we’ll turn to in module two,
SWIFT is an organisation,
which provides protocols
to enable communications
between domestic digital payment systems.
In addition to these developments in payments,
we also see, in 1971,
the establishment of NASDAQ.
NASDAQ was the world’s 1st electronic stock exchange.
Today, there are almost no stock exchanges
or other financial exchanges,
which are not electronic.
Basically, in the context of both payments,
as well as, in stock and other securities markets,
the process of digitization,
which began in the late ’60s and early 1970s,
has fundamentally transformed
the way these markets work,
so much so that today, it is very difficult
to find a human being actually trading securities
with another human being in stock markets.
At another level, if we think of
today’s global foreign exchange markets,
markets in which people buy one currency,
like the dollar and sell another,
like the yen or the RMB,
these markets today
do approximately 5.5 trillion dollars
a day, every day of the year in transactions
and almost none of this
takes place in the form of cash.
It is almost entirely digital book entries
taking place between the computer systems
of major financial institutions.
This process, however,
is one that was in fact
well established by the late 1980s.
A major market crash occurred in 1987.
That market crash today,
our best explanation,
is what is called programme trading.
Programme trading involves preset computerised
buy and sell orders,
and so when stock prices
drop to a certain level,
this would trigger automatic selling
by computer programmes,
which would then trigger more price drops,
triggering more sales and eventually resulting
in our first major coordinated
global market crash.
Now, across the 1980s,
we began with the origins of online banking
in parallel to the emergence of the internet
and certainly by
the beginning of the 21st century,
a number of banks already had
over a million plus online banking customers,
but today, we are waiting to see
what will be the first financial institution
with over a billion online financial services customers.
cross-border transaction and correspondent banking.
As individuals, you may have experienced
in the context of remittance,
however, this only represents
a very small fraction
of the global financial transaction initiated
and settled through payment network such as Swift.
Finally, the payment module
will open by introducing the development
of a new payment stack
which has been made possible
by the advancement of a new technology
such as DLT, distributed ledger technology,
but also increasingly being justified
by recent risks identified in the payment rail system
that is now over 50 years old.
This final module will provide you
the basis of operation
of a decentralised system,
its difference with the current status quo
and its potential to complement
or replace the current infrastructure.
Module 2 Learning Objectives
Module 2 introduces the spectrum of electronic and other means of payment, from the traditional (cash) to the most recent (Bitcoin and cryptocurrencies). It highlights to learners the evolutionary context as well as the transformative role of new technologies in both traditional payments as well as alternative money and payment systems and the role of these in our daily lives and in our broader economies.
In Module 2, learners will:
Consider the evolution of payment and money, from paper to digital to cryptocurrencies and beyond.
Understand the underlying infrastructure of both traditional and new forms of payment.
Understand how technology is transforming payment in developing countries and how it is changing payment in developed countries.
Analyze the role of blockchain and cryptocurrencies in developing new means of money and payment.
2.2 Individual Payments
We will now be looking at
the evolution of payment
from the perspective of usage of an individual.
Most of us are using money
to make and receive payments.
However, technology has changed
both the form of money takes,
but also how it’s being transferred.
In short, you can think of four phases
that we will all discuss in more details.
This first phase was barter.
The second phase was commodity money.
The third phase was coinage.
And the fourth phase was dematerialized payments.
The evolution of money
as a mode of payment
is over 10,000 years old.
The first phase started in 9000 BC,
up until 600 BC.
It was the one of barter.
People directly exchanged goods and services.
There was no money as a standard
or medium of exchange.
This system, however, had limitations,
including the capacity of carrying goods,
and transporting it across long distances
to make exchanges
or standardise commerce.
This evolved into the second phase
of commodity money.
With certain items being selected
to perform transaction.
For example, in 1100 BC,
China used small cast replicas
of goods, token, made out of bronze.
However, you may be more familiar with
example of other commodities being used,
such as silver or gold.
Even the word salary
is actually related to that era.
Back then, Roman soldiers were paid in salt
and their salt was a salary.
From 600 B.C.,
coinage has been introduced in Lydia, today’s Turkey,
but it will take another 1000 to 2000 years
until paper money starts to be introduced,
first in China, in the 700s,
and then in Europe,
with Sweden leading the way in the 17th century.
Paper money represents an example of
how technology, the printing press,
allowed to store varying amounts,
from one US dollar to 100 US dollar,
on the same size of paper.
In addition, European banks started to guarantee
that the bearers of bank notes
had to be paid in gold equivalent.
One of the previous limitation of money,
that it was light,
and therefore flying away in the wind
when you do commerce,
became an advantage,
because now instead of paying cargos
with heavy bags of coins
that were inconvenient and risky,
you could pay with small bills
that were light and stored a lot of value.
The dematerialization of payment
increased in speed and method
from the 1950s onward,
as technology has become more commoditized.
Payment was increasingly digital,
and the volume of non-cash transaction
has been steadily rising ever since.
Here is a timeline of how
payment innovation has evolved,
and with it, your behaviour.
1946, the first credit card is created
with it before being popularised by
card networks in the ’50s.
The chequebook is introduced
about the same period.
the ATM network becomes
interconnected and global
with millions of withdrawal points available.
In the 1990s,
the EMV standard creation allows
to enhance security
and data storage on cards.
In 2000, mobile money
provides a payment solution in developing countries,
or banking services in developed nation.
The dot-com period
and the rise of internet
and e-Commerce companies
has started the trend of complete digitization
of cash transaction.
Plastic cards have been replaced
by e-wallets or virtual cards.
But it kept on going.
2009, Bitcoin is created
as a decentralised currency,
stored across the internet.
2010, introduction of
contactless cards and payments,
which seven years later,
are one of the most popular method of payment.
the popularisation of wearable devices,
mobile wallets and cryptocurrencies continues.
Payment methods have changed
and with it all behaviours.
From the Amazon supermarket without checkouts,
to Venmo true-value proposition
of not being a bill splitting app,
but instead addressing the social stigma
to request for money.
Predicting the future of payment is difficult,
and it is not anymore linked just to money,
but to technology itself,
that is changing the methods
available to you.
were not even considered
less then a decade ago.
And even today,
data is becoming the new oil
and allowing a new type of barter system
where people are giving
their personal information,
in returns of goods and services.
Therefore, payment and money
is constantly evolving,
even more rapidly today.
And we will point you toward
the further reading materials
for you to keep updated
your knowledge on that sector.
2.3 Developing Countries and DFS: The Story of Mobile Money
Hello, I’m Ross Buckley,
the King and Wood Mallesons Chair
of International Finance Law at UNSW Sydney,
and I’m here to talk to you about
the story of mobile money
and digital finance in developing countries.
I want to tell this story in terms of four questions.
What? Where? How? and Why?
So, let’s start with “What?”.
What is mobile money?
Well, it’s e-money.
It’s an electronic credit on a mobile device
that represents a unit of real money
that typically sits in a trust account
in a commercial bank somewhere.
So, it’s an electronic representation of paper money,
and that being electronic allows you
to do all sorts of useful things with it,
like save more readily,
send money back to your village,
do all sorts of things that
otherwise would take you time
and cost you more money.
Where was it developed?
Well, it came about because one day,
telecommunications companies realised
that their software tracked in real time airtime credits.
They knew exactly how many minutes
a prepaid customer still had in credit,
and if they knew that,
they knew how many currency credits the customer had.
They realised this software could well adapted
to do the same thing.
And so, mobile money was born.
Initially, in a in a few countries,
including the Philippines,
but it achieved lift off in Kenya a decade ago in 2007.
It was a Vodafone product, M-Pesa,
and within three or four years,
the majority of Kenya’s GDP was flowing
through M-Pesa every year.
The “How” question, how does it work?
Well, it’s based on cash-in and cash-out agents
who are the same small shopkeepers who sell airtime.
These are tiny stores,
maybe three metres by three metres,
by the side of the road
that sell tinned meat and soft drinks,
and cigarettes and airtime,
and is also act as agents for the mobile money service.
So, a customer can walk in,
pass some cash across the counter,
and get a credit
that shows up immediately on their phone
and they can walk away happy,
knowing they’ve got
some electronic money on their phone
that they can do things with,
that they couldn’t so readily do with the paper money.
That’s one way a credit comes about,
but the more often way is
the government makes a transfer payment,
a welfare payment directly to the phone,
because paper-based payments
have all sorts of problems for governments
in developing countries,
and electronic payments
are a much more efficient method.
Often, central banks will want
this service offered by their banks
because they trust the banks,
they know the banks,
they know how to regulate them.
But, banks around the world
are not typically good at providing services
to poor customers.
The entities that are already doing that
are the TELCOs.
They are very efficient at selling services
to very poor people and generally,
if you’re a government,
you’ll get the innovation
in the mobile money space
from your telecommunications companies
more than your banks.
The fourth question is,
why does it matter?
And it matters because without electronic money,
paying an electricity bill or a school bill
can require a parent to take the whole day off work,
travel for a few hours on a bus,
stand in a queue for a few hours,
travel a few hours home.
This is not typically transformative
for people who live in the capital city
or in the big cities,
it transforms the life of people
who live in the countryside
who don’t have access to bricks and mortar branches.
It enables them to send money home to a relative,
to save money safely,
to pay bills in their microbusiness,
whatever they need to do,
to do it quickly and safely and cheaply.
In some states in India,
before mobile money was introduced,
up to 45% of government welfare payments
would go missing
because you had a largely illiterate population
and a paper-based system.
The electronic auto trail of mobile money
gets around that problem.
In Papua New Guinea,
teachers are regularly taking off days off school
so they could cash their paychecks,
before mobile money systems allowed them
to get access to their pay without having to,
again, get on a bus for a few hours
to the nearest major town.
The buzzword is financial inclusion.
It’s about including these previously excluded people
in the financial services
and allowing them to do things more efficiently,
and thereby promote economic growth
and reduce poverty.
What are the challenges with it?
Well, it’s worked well in east Africa.
Kenya, Tanzania, Uganda,
it’s worked very well.
In other places, the record is more patchy.
In many countries,
governments transfer payments
to mobile money accounts,
people go to the cash-in and cash-out agent,
take the cash out, and then transact in cash.
That fails to realise most of the goals
of financial inclusion, you know,
they’re not getting the efficiency benefits,
you’re not getting the network effects,
you’re not getting the vibrant ecosystem,
and it also causes a real problem for agents
because agents are mainly handing out cash,
and unless the other part of their business
provides them with a ready inflow of cash,
they’re going to run out of cash often
and they’re not going to be able
to meet redemption requests,
and then people get frustrated with the system.
So, lots of people around the world
are working to try and solve this.
They’re working by developing
new technological products,
customer education programmes,
or in the case of the programme I lead at UNSW,
we work with central banks
in partnership with the UN Capital Development Fund around the world,
we work with poor country central banks,
helping them get their regulation more adapted,
more suitable to digital finance.
But, there are main roadblocks
to vibrant digital financial ecosystems
in many countries,
and one of the fundamental ones of these
is a failure to adapt products to local needs
and to understand local customer journeys,
and I’ll discuss this with you later.
2.4 Developing Countries and DFS: Regulation of Mobile Money
Hello I’m Ross Buckley,
the King and Wood Mallesons Chair
of International Finance Law at UNSW Sydney.
I’m here to talk to you about
the regulation of mobile money
and digital financial services in developing countries.
The first thing we need to do
with mobile money is keep it safe.
We need to protect the float.
The float being the body of actual money
that represents the electronic credits
on the mobile devices.
In Common Law countries,
this is relatively easy to do,
using the institution of a trust.
You have a trust deed which
we’ve given you a version of
in the publication that’s up on your slides.
There’s a trust deed,
either money is deposited
under that deed in a commercial bank,
and the deed provides for the role of a protector.
The protector will typically be the Central Bank,
although it could be somebody else.
They will have the duty of
inspecting the trust arrangements
and insuring, most importantly,
that there is a 1:1 ratio
between issued electronic money
and actual money sitting in the trust account,
various other rights of the protector as well.
And the protectee you need
because the beneficiaries of the trust
are the customers,
they’re not going to understand all this,
and they’re not going to enforce their own rights.
So as I said, in common law countries,
relatively easy to do.
In Civil Law countries, not so simple,
because usually the trust is not there.
But you can get to the same end
using a mix of mandates,
contracts, fiduciary contracts,
and sometimes direct regulation as needed.
There’s no one size fits all solution
for Civil Law countries,
you need to craft a solution for each jurisdiction.
But again on your slides,
there’s an article that we’ve written,
that you can easily draw down off SSRN,
that deals with all of those technical issues.
Once you’ve kept the float safe,
there’s only two other, in my view,
core pieces of regulation you need.
One is Consumer Protection Regulation
and the other is Money Laundering
and Terrorism Financing Regulation.
You need the latter because you need it.
There’s an international regime
that you eventually see a country on a blacklist
if it doesn’t have adequate AML/CTF regulation.
And the important thing to do in dealing with that,
is to use proportional risk-based assessment.
In most poor countries
you are not going to be able to comply,
you’re not going to be able to
reach the very highest standards of
You’re going to have to, you know, cut some corners
and decisions are going to have to be made.
But the international regime allows for that.
The problem often is that regulators
try to enforce standards that are higher
than is required by the international regime
and are not appropriate for their own countries.
So we need a little bit of regulatory innovation
and courage often in that space.
Consumer Protection is
essential for the substantive reason
that without it,
people won’t trust the system.
If people are losing their money,
they won’t keep using it.
And mobile money and digital finance
needs network effects.
It needs a lot of people using it
so that it’s beneficial for everybody.
The easy part for consumer protection is rules.
It’s relatively easy to craft a good set of rules.
The difficult part is recourse mechanisms.
People have got to have some way
to get their problems solved.
And any effective recourse regime, in my view,
needs a free-to-call telephone number
that’s manned a good number of hours a day
by someone with authority
to resolve customer problems.
If people have to pay to make the call
and wait a long time online,
they’re going to hang up.
If it’s a free call
but it’s not answered for an hour,
they’re going to hang up.
They’re not going to get their problem solved.
And central banks need to police this.
Central banks actually need to
sort of play the role of mystery shopper, I think,
and pretend to be customers and find out
how well these recourse mechanisms work.
Because they’re really essential to consumer trust
in a digital financial ecosystem.
I also wanted to talk about the difference
between FinTech in rich countries
and digital finance in poor countries,
because they come from quite different places.
FinTechs in rich countries are typically startups.
They’re young entrepreneurs seeking to get rich.
Digital Financial Services in poor countries are partly,
occasionally driven by that, but not so often.
They’re mostly driven by government policy.
Governments have embraced
this idea of financial inclusion
and they’re encouraging their banks
and their telcos to provide it.
So rich country regulators
are trialling regulatory sandboxes,
safe places in which entrepreneurs can take risks
with reduced licencing obligations,
and they’re trialling other measures
to promote innovation.
And in some poor countries,
regulators are doing this,
but something much more is needed
of regulators in poor countries.
And that’s a major mindshift,
a major change of perspective.
So that they actually get out of the capital city,
they get into their own villages
where these innovations are needed,
and they talk to their own people and find out
what the customers in their countries really need.
Because this is what’s holding the development
of Digital Financial Ecosystems up in many countries.
It’s the offering of inappropriate products.
These countries are small.
The telecommunications companies
naturally want to roll out the same products
across multiple countries,
but the local Central Banks’ role in part
is to encourage the provider
to adapt the product for the local market.
And that’s a deeply different and innovative role
for a central bank
and one that they’re not accustomed to do.
But experience teaches us if they don’t do it,
it’s a real risk that the innovation will not thrive.
We’ve written about the customer journey,
we’ve written about building consumer demand
for Digital Finance.
You can find those references on the slides.
So I just want to conclude by emphasising
that for Digital Finance
to flourish in many countries,
a big change of mindset is required by the central bank.
Asia Capital Markets Institute based in Hong Kong.
I’ve been a corporate finance lawyer for nearly 20 years,
having worked in New York and California
during the dotcom period
at Wall Street firm Sullivan & Cromwell,
as well as, at Credit Suisse
where I worked on some of the largest
and first-of-its-kind transactions.
These included the IPOs of CCB and ICBC,
the privatisation of alibaba.com
and the setting up Credit Suisse’s investment banking
joint venture in China.
And since I was in Silicon Valley,
and now at a FinTech-focused startup,
I think I can just lose the tie.
Alternative finance, according to the
Cambridge Centre for Alternative Finance,
can broadly be described as
comprising financial channels and instruments
that emerge outside the traditional financial system.
In my chapter in The Fintech Book,
which has recently been translated
and published in China,
I have called some of these emerging
financial channels, online capital marketplaces.
Alternative finance can also refer
to the new investment classes
being sought to be created,
including equity and loans to SMEs,
and more recently, crypto assets.
In this module, we will discuss the following.
First, a brief history of finance and its innovations.
Second, the digitization of financial services.
Third, the opportunities and challenges
of distributed peer-to-peer models of finance,
such as crowdfunding,
marketplace lending and token sales.
Fourth, the rise of the Chinese TechFins
in its new models and products.
And five, how the banks are now positioning themselves
as technology companies.
Module 3 Learning Objectives
Module 3 addresses the digitization of finance and investment and the development of alternative finance and investment mechanisms, including P2P lending, crowdfunding and initial coin offerings (ICOs).
In Module 3, learners will:
Understand the digital transformation of traditional securities and investment markets over the past 30 years.
Consider the major forms of technology impacting finance today.
Analyze the evolution of major forms of alternative finance, including P2P and marketplace lending, crowdfunding and initial coin offerings (ICOs).
Learn about new business models, particularly the impact of technology firms entering into finance.
3.2 A Brief History of Financial Innovation
Finance can and should be
an enabler of commerce and investment
in growing the real economy
as well as to provide individuals
with more personal security
against life’s vicissitudes.
Money or currency consists of
a stored value instrument that is scarce.
Throughout the ages,
these have ranged from cowry shells
to gold and minted metal coinage.
In the west,
money lenders have existed since the Roman Empire
where individuals have made
private loans to those who needed it.
There has long been widespread
religious and moral condemnation against usury.
The charging of interest for loans
with different religions taking different stances
on the permissible extent of such interest.
For example, the Islamic faith prohibits
any charging of interest whereas
the Judaic faith permits charging interest to non-Jews.
Modern legislation focuses more on setting limits
on the amount of permissible interest set.
Florence sat at the centre of the Renaissance
due in part to the extensive trade
through the Italian city
leading to over 100 banks being there
by the end of the 14th Century.
The Medici banking empire
was one such example
and was the banker for the Catholic Church.
Its expansion was aided by the invention
of double-entry bookkeeping by
Franciscan friar, Luca Pacioli
which allowed for reliable documentation
for both creditor and debtor.
The important role of ledgers
will be revisited with the advent
of blockchain technology in this century.
The Tang and Song Dynasty emperors
in the seventh century started using paper
which their forefathers in China invented
as bank notes or jiaozi,
that were originally used as promissory notes
and became a lightweight alternative
to heavy and metallic coinage.
To address counterfeiting,
a variety of techniques involving different inks,
papers and seals were used.
Property and being able to prove ownership
is critical as sources of collateral for finance.
Pawn shops such as the medieval practise
of so-called Lombard banking
dealt in the delivery of goods and custody
and many legal jurisdictions developed
centralised land record registries
that enabled more lending to take place.
As international trade grew,
the financing of those ocean expeditions
of vast durations and distances
involved substantial risk.
With the invention of the joint stock company
shares and bonds could be
issued to the general public to fund projects
and the Dutch East India Company
listing in the Amsterdam Stock Exchange
paved the way in the 17th Century.
Similar venues were set up across the ocean
at coffee shops along London’s exchange alley,
and under a buttonwood tree on Wall Street,
and sea captains and merchants also met
at Lloyd’s Coffee House at London’s Tower Street.
These soon evolved into powerful institutions,
today’s London Stock Exchange,
New York Stock Exchange
and Lloyd’s of London
with impressive architecture to reflect their influence.
Within these buildings,
trading runners or deer would dash to
nearby brokerage offices
to deliver stock quotes
from the brokers on the exchange floor.
With the invention of electricity,
technologies like the telegraph
and then the telephone soon meant
proximity to the exchanges proved less critical.
In 1971, Nasdaq became the first electronic exchange
with many other securities and
commodities exchanges following suit.
A new electronic and digital era was dawning.
3.3 Digitization of Financial Services
Financial investment and trading
can best be understood as
comprising of two groups.
First, retail investment and trading
of public companies is conducted
by the general public on exchanges
through brokers and dealers.
Second, institutional investment and trading
involves large financial institutions
who buy and sell publicly listed securities
between each other.
In the meantime,
the investment into private companies
has been the remit of specialised investors,
such as venture capital and private equity firms,
with so called exit or liquidity events
being an initial public offering
into the public markets
or trade sale of the business.
The venture capital firms
on Sand Hill Road in Menlo Park, California,
were likely instrumental in investing and growing
what become known as Silicon Valley.
Including their successes in hardware
being turned into software and the internet
that gave rise to the dot com boom.
One major development at that time
was when online brokerage firm E*Trade
was able to get allocations
to some of the hot tech IPOs.
Electronic banking also started expanding
as major banks extended services to consumers
who were increasingly coming online
with the lowering costs
and increasing ease of access.
At the same time,
institutional trading expanded dramatically,
with alternative trading systems and dark pools
allowing financial institutions to trade
large blocks of securities at one time
to minimise pricing impact.
At the same time,
proprietary trading at desks invested and develop
automated algorithmic trading programmes,
as well as high frequency trading programmes
that use computational power
to devise trading strategies
and automate trading in ways no human could.
The global financial crisis in 2007
changed the landscape dramatically.
After the bursting of the sub prime mortgage bubble,
and the rapid devaluation of financial instruments,
such as mortgage backed securities
and credit default swaps,
many major financial institutions collapsed,
or needed to be bailed out by their governments.
Lending then dried up by many traditional lenders,
and many governments sought to encourage
the emergence of new players and business models
that provided alternative finance
to individuals and businesses.
At the same time,
2010’s flash crash
saw the Dow Jones Index drop
nine percent within minutes
and was subsequently attributed
to high frequency traders initialling,
that placed thousands of orders
with intention of cancelling them.
The automatic shutting down
of many automated computerised traders
due to the extreme drop
also led to a rapid loss of liquidity.
In the European Union,
challenger banks were encouraged
and has culminated
in open banking policies such as
the payment services directive,
which forces banks to produce customer data
to make it easier for customers
to switch financial providers.
Some of these banks were also virtual banks,
with no branches at all.
In the United States,
a stark reminder that
it was not business as usual
can be seen in the before and after pictures
of UBS’s famed trading floor in Stanford, Connecticut.
This was once the largest in the world
with 5,000 stock, bond and currency traders.
The global financial crisis resulted,
amongst other things,
in the passing of Dodd-Frank,
which prohibited proprietary trading by banks,
and the Jump Start Our Business Startups Act,
or JOBS Act in 2012,
which legitimised the concept
of equity crowdfunding in law.
The era of distributed peer-to-peer models
of finance was born.
3.4 FinTech and Funds
As we all know, FinTech is really disrupting
many verticals of the financial services industry,
from investment banking,
asset management, retail banking.
But one area that it’s also affecting as well
is the hedge fund industry.
For many years, the hedge fund industry
was the most entrepreneurial,
at the cutting edge of technology.
And obviously, a lot of Fintech innovations
are benefiting the broader hedge fund industry as well.
There’s many examples.
Think about it.
Imagine I’m a fund manager.
Normally I try to access data
that is available publicly
and I’m trying to use.
But what if I can use some of the big data analytics
and artificial intelligence solutions
to try to really generate insights?
Imagine using satellite imagery to try to look at
what’s happening on, let’s say, on oil containers
and how much oil there are on the ships.
Or actually looking from satellite imagery
how many cars are in the parking lot
and use that to try to actually forecast
a company’s actually earnings.
Or imagine being able to use
some of the latest technology to read,
go through all the news articles
around the world available online
or all the tweets and Facebook messages
and be able to come up with a sentiment analysis
of what are people really seeing
and thinking around the world.
Well, what’s really interesting as well,
that’s one example
but there are many other examples.
For example, today a lot of people
want to get access to research.
A normal hedge fund manager would actually try
to work with an investment bank,
will actually execute some of their traits
via an investment bank to get access
to some of their research analysts.
But if you think about it,
it’s not very efficient.
It’s a bit like buying a CD if you like one song.
What now we’re seeing the emergence
of independent platforms.
If you think about the Spotify of research
where investment analysts can actually go there,
put your own research.
And if customers like them,
they just go pay for the research and pay
for that specific investment analyst.
A bit like if there is a particular song or artist
that you like on Spotify and
you actually want to listen to their music.
But in many cases we are seeing
really the FinTech industry transform
how business was currently done.
Let me give you an example.
Let’s say I want to launch a quant fund,
a quantitative trading fund.
Today I have to rent the office space,
hire a couple of PhDs who’ll do research
and build my team together with me in one location.
That is a great way but doesn’t really give you access
to the best talent in the world.
What if you can actually crowd source
and actually crowd fund
and be able to get access
to talent from all around the world?
Some of the best PhDs or quant traders
from around the world and try to get their models
that you can actually use.
Well, we are seeing some players do exactly just that.
Well now you can launch a quant fund
where you going to get some of the best people
from all around the world
to actually submit models.
And if the model is quite successful,
give them portions of the revenues made.
Doesn’t matter if you are in Santiago, Chile,
in Moscow, Russia or in Singapore, Hong Kong.
You can have as much of a chance of
being discovered from a talent perspective.
But also it’s affecting,
FinTech is affecting the way business is done.
Today, anybody launches a hedge fund or a fund,
will generally try to go raise money from investors.
That service is now offered by investment banks
who will try to match hedge fund managers
and hedge fund investors.
The way that process works is very inefficient.
Often via conferences and
personal meetings or emails,
it’s actually not very efficient.
We are now seeing the emergence of online platforms
where these two branches can actually connect.
And actually you have analytics
and data on who can be a good fit for each other.
So as you can see there’s a lot of,
really lots of really exciting and interesting innovations
taking place in the FinTech world
that are also affecting, impacting positively,
in certain cases negatively the hedge fund industry.
Definitely an area to watch.
Industry Showcase: How AI is Transforming the Future of FinTech (Microsoft)
Hi, everyone. This is Delon.
I’m a Technical Evangelist
from the Commercial Software Engineering team,
Today, I’m here to present to you
how AI technologies
and big data analytics could integrate
with your FinTech technologies.
Smarter computers, algorithms,
and dedicated AI systems
enable smarter decision-making,
For example, recognise predictors
of financial turbulence.
And the combination of artificial intelligence
and big data,
allow us to understand better how people,
how they spend the money, their health,
as well as the lifestyle.
For example, intelligent banking.
It is all based on the “always on” customer,
and predicting his or her financial needs
at a certain point of time.
Thus, it’s not just about the bank,
but providing products and services
through one channel,
a single interface that can maximise
your customer experience,
and hence centricity.
However, building those trusts from your customers
is still one of the biggest challenges for FinTech.
And in Microsoft, we are trying our best
to gain and build lots and lots of trust
from our customers.
And here are a few examples:
If P&C Insurance is a world-leading
property and casualty insurance company
that serves over 3 millions of customers
in the Nordic region,
if actually leveraging our Cortana Analytics Suite
to perform Churn analytics.
In other words, predicting whether their customers
will cancel the policy in a 40-day window
surrounding the renewal dates.
Another example is that,
actually they’re doing upsell prediction.
Basically, finding the probability of success
of upsell communication to a given customer.
Last but not least,
they are applying text analytics algorithms
on their inbound emails from their customers.
So, they could now better understand their customers,
classify those emails,
and better serve their customers.
Another example is Novum Bank.
Novum Bank will like to
assess the credit-worthiness of their customers.
Especially in the poor credit information markets.
With Microsoft, they actually built
an automated psychographic credit-scoring engine,
running on Azure, for onboarding.
In return, the acceptance rate was increased by 18%,
and they actually rejected
Finally, I will like to share with you one more example.
Mid-Point Message from Course Director Douglas Arner
Hello, and welcome to the mid-point
of Introduction to FinTech.
We very much hope that you’ve enjoyed
our first three modules so far.
Looking back, in our first module,
we looked at what is FinTech?
And since that module came out
the World Bank has released the 2018 Global Findex
which highlights how over a billion people
over the past six years
have opened new bank accounts,
much of this as a result of FinTech.
In module 2, we looked at payment,
cryptocurrencies and blockchain
and over the past six months
the idea of cryptocurrencies of blockchain
has continued to dominate
the news media about finance.
Countries such as Venezuela
have launched the first sovereign cryptocurrencies
and others, including China, Russia
and a range of others
are considering similar proud programmes.
In module 3, we looked at the digitization of finance
and at alternative finance.
In the context of digitization of finance,
we’re seeing an increasing presence of AI, the launch of
an increasing number of Independent funds
which choose their own investments via AI
and run themselves.
In the context of alternative finance,
the use of blockchain and crowdfunding,
the idea behind ICOs continues to grow and grow
but with evermore regulatory attention.
So far, over 50 regulators around the world
have made warnings and announcements about ICOs
but at the same time,
even though there are dangers
there are huge opportunities
for funding new ideas and early-stage companies.
Looking into the second half of the course,
we’re going to move into issues of regulation
but also one of the most exciting areas
of opportunity in FinTech
and that is the entire RegTech ecosystem in module 4.
In module 5, we’ll turn to
some of the biggest challenges,
some of the challenges very much highlighted
by recent issues with data companies
such as Facebook, Equifax and others.
The question of data, monetization of data,
cybersecurity and TechFins
and finally, in module 6,
we’ll look at a number of specific case studies
using these to pull together
the things that we’ve learned
over the previous five modules and look forward a bit
to see what the future of FinTech may hold.
For those of you
who haven’t quite finished module three yet,
we encourage you to please continue
going with the course.
After all, you have plenty of time
and can do this at your own pace
but once you’ve started it,
you might as well keep going because the more you do,
the more you learn
and the more you’re likely to understand
this world of financial technology around us.
And for those of you who still haven’t quite started,
now is the best time.
Module 4 FinTech Regulation and RegTech
Welcome to Module 4
4.1 Module 4 Introduction
In Module 4, we turn to regulation of FinTech,
and a new development,
what is called RegTech or regulatory technology,
and if we look at this module,
we’re going to begin by looking at
why do we regulate financial markets
before turning to the particular challenges
of regulating FinTech.
From there we’ll move on
to what we think is one of the most exciting areas
of interaction between finance technology
and regulation, and that is RegTech.
We’ll look at the RegTech ecosystem
and also ideas of smart regulation
and the outlook for future design of financial systems.
Module 4 Learning Objectives
Module 4 addresses the range of regulatory considerations and approaches in the context of FinTech. It highlights the core regulatory objectives and the relationship between the post-2008 financial regulatory reform process and FinTech. It focuses in particular on the concept of RegTech – “regulatory technology” – and the RegTech ecosystem comprising financial institutions, startups, and regulators, and introduces “Smart Regulation” as the redesigning financial infrastructure and regulatory systems on the basis of new technologies, including Big Data, cloud, AI and blockchain.
In Module 4, learners will:
Understand major financial policy and regulatory objectives and their implications for FinTech.
Explore RegTech and the RegTech ecosystem, in order to understand one of the key trends in financial and regulatory transformation.
Consider how regulatory systems and financial infrastructure could be redesigned on the basis of new technologies to deliver better results both from the standpoint of efficiency as well as resilience
Think about new regulatory approaches such as regulatory sandboxes, India Stack and Smart Regulation.
4.2A FinTech Regulation (Part 1)
The first question is really
why do we regulate financial markets in the first place?
even why do we regulate financial markets at all?
And as we’ve seen
with fintech’s evolution generally,
the 2008 global financial crisis
was a game changer
for financial regulation.
Prior to the 2008 global financial crisis,
there was a general consensus
in favour of largely market-based approaches
to financial market regulation.
Many of these derived from
the ideas of what is called
the efficient markets hypothesis.
The idea of the efficient markets hypothesis
is that markets will price in
all available information
with prices of financial assets
providing for efficient allocation
of financial resources.
The idea is that market mechanisms provided
there is sufficient information
available to markets
will function to
both price, financial assets,
as well as, allocate financial resources
to their most valuable uses.
That idea was based on
a number of assumptions.
First, that there is
perfect information available in markets,
second, that there are
no transaction costs in markets
third, that there is
perfect competition in markets,
and fourth, that there were
rational market participants.
All of these ideas together
would lead towards
efficiently functioning financial markets
which would properly
support the functioning
of the overall economy.
The problem was that
certainly even prior to 2008,
we already knew that
information was not perfect,
that there are transaction costs
in acquiring information
or enforcing transactions in markets,
and that competition
is by no means perfect.
And much of law and regulation
prior to the global financial crisis
focused on improving information quality,
on enhancing competition,
on reducing transaction costs
to reduce what are called market failures.
The idea is that law and regulation
would be used
to reduce problems in markets
to help those markets
function in a better way.
2008 global financial crisis
fundamentally changed the way
that we think about
finance and its regulation.
In particular, the 2008 global crisis
and the hundreds of billions of dollars spent
on bailing out large banks around the world
highlighted a problem
which is called systemic risk.
Systemic risk is the risk that
the collapse of an individual financial institution
will cause the collapse of
the entire financial system,
which will in turn
cause the collapse of the economy.
This is exactly what we saw in 1929
and the 1930s Great Depression.
It’s also what we saw in 2008.
As a result, since 2008,
there has been a massive amount of attention
on building new regulatory frameworks
to prevent financial crises,
to build confidence,
and to make markets function properly.
So, if we think of regulation today,
its function continues to be
on improving market functioning and efficiency.
It is also about
preventing systemic risks,
maintaining financial stability,
as we’ve already seen,
it is about fairness
to market participants.
4.2B FinTech Regulation (Part 2)
As we just discussed,
the 2008 financial crisis was a game changer
in the way that we look at
regulation of financial markets.
And for the eight or so years after 2008,
policy makers globally,
as well as, regulators around the world
spent much of their time and efforts
on developing new regulatory systems
to prevent the sorts of crisis
that we saw in 2008,
but this 2008 crisis
also triggered an explosion
in the development of fintech.
And that explosion
in the development of fintech
has been a major challenge for regulators,
after all, one element of fintech
is the idea of
disrupting traditional institutions,
but a major object of financial regulation
after the 2008 crisis
has been preventing disruption
in financial markets,
and the financial system.
The same time,
there has been a strong focus on regulation
to support innovation and development
in the financial system,
and so regulators have been forced to
come to terms with the explosion
in new technologies and new participants
in the financial sector,
and to come to grips with
how to regulate the opportunities
as well as, the challenges.
And so far what we have seen
are four major approaches
The first approach has largely been
one of doing nothing,
in many ways,
this idea of doing nothing
can be seen as
either a positive or a negative approach.
It can be either permissive or restrictive.
China, prior to the middle of 2015
is usually seen as the major example
of a country taking a permissive approach
not to put in place new regulations.
And in many ways,
it was this decision,
which has allowed the explosion of fintech
in the context of China.
But, as we’ve seen before,
that explosion of fintech in China
also brought with it new risks.
The evolution from
too small to care to too big to fail,
that we’ve seen
in the context of payments,
and other areas.
As a result, even in the context of China,
the decision over the past several years
since 2015 has been increasingly to build
a new regulatory framework
for digital financial services.
have taken an approach
of not doing anything,
which has largely been restrictive,
requiring new entrants into financial services
with new business models,
and new approaches
to comply with existing regulatory requirements,
which would typically develop
for a very different type of
established financial institution,
and the like.
The end result of this is
often a very restrictive approach.
Coming into the past several years,
regulators have been trying
to balance the objectives
of innovation and growth
with considerations of financial stability
and consumer protection,
and as a result,
they are developing an increasing number
of experimentation-based approaches.
Some involve regulators
establishing contact points
to meet with new entrants,
to learn about technologies
in order to be able to
develop appropriate regulatory responses.
Others have developed
what are called sandboxes,
these are areas for experimentation
in a limited market context
with limited regulation,
in order for both the new company
as well as the regulator,
to learn how best to move forward.
an increasing number of jurisdictions
developing new regulatory frameworks,
particularly for the sorts of things
that we’ve seen in Modules 2 and 3.
Things like P2P lending or
alternative payment systems
or forms of crowdfunding.
But jurisdictions like China, India and others
are also looking at
developing entirely new regulatory approaches.
4.3 Evolution of RegTech
The 2008 financial crisis
was one of the key triggers
for the massive acceleration
in the development of FinTech worldwide.
But in addition,
it was the catalyst for the development of
RegTech, or regulatory technology.
And the ideas of RegTech relate to FinTech
but are much more broader.
In other words,
RegTech is the idea of using technology
for regulatory compliance,
but also, regulatory design.
It is the idea of using technology
to make financial markets
and their regulation more effective.
Now, if we think of RegTech in that way,
the key aspect is that
it is beyond FinTech.
RegTech can apply
in any segment of the economy,
not just in the context of financial regulation.
One can imagine
the application of technology
to environmental regulation,
or traffic regulation,
or airline regulation.
Any area of the economy with regulation,
the application of technology
offers the potential
to transform the effectiveness
of that particular regulatory system.
However, in the context of finance,
RegTech is so far,
the most developed.
In particular, we see RegTech
across traditional financial institutions,
across new startups,
and also across regulators, themselves.
And the global financial crisis
really made this a necessity.
we have seen an absolute explosion
in new regulations around the world.
New regulations are released
by a major jurisdiction
approximately once every hour.
The end result is that,
thousands of new regulations come out.
And, for a financial institution
doing business on a cross-boarder basis,
this is one of the most complex influences
on their operations
as well as on their profitability
because, not only is regulation a challenge,
but it is also a cost.
As a result,
the global financial crisis
and its explosion of regulation
has driven the established financial industry
into applying technology
to address their compliance burdens
and their compliance costs.
At the same time,
we have seen an explosion
in new startups, new firms,
which are offering technologies
to help both startups
as well as traditional financial institutions
and even regulators
to better address
their regulatory and compliance burdens.
Finally, regulators themselves
are increasingly using technology
for a range of purposes
not only to do a better job
in their regulatory functions,
but also to increase market efficiencies
and reduce cost in the industry.
4.4 RegTech Eco-system: Financial Institutions
The RegTech ecosystem encompasses
both industry and regulators.
the traditional incumbent financial institutions
and investment banks.
It also encompasses startups
of an increasing range
and it also encompasses regulators.
However, most of the development
that we’ve seen in the area
of RegTech over the past decade
following the global financial crisis
has been focused
in the traditional financial services industry,
in particular, in the banking industry.
And we can see this obviously simply
from the cost of regulation.
Not only have we had thousands of
new regulations in the US,
in Hong Kong,
in economies all over the world
as a result of the global financial crisis.
But large financial institutions around the world
have paid to date over $300 billion US dollars
in fines for regulatory failures
both resulting from and also in the aftermath
of the global financial crisis.
Compliance has become a major challenge
from the standpoint of the industry
both from the standpoint of
simply keeping up with regulatory changes
and their implications,
but also from the standpoint of
the very, very large fines
that we have seen
coming out of major regulators
since the global financial crisis.
And if we look today at
traditional financial institutions,
if we look at RegTech in banks,
probably two areas highlight best
the necessity of technology.
First, are what are called
know your customer requirements.
Know your customer requirements
are something that every financial institution
everywhere in the world must comply with.
The origin of
know your customer requirements
comes from a series of regulations
that focus on preventing criminal use
of the financial system,
things such a money laundering
or the financing of terrorism.
Internationally, we have an organisation
which is called the FATF.
The FATF is the Financial Action Task Force
on Money Laundering
and it establishes
internationally agreed minimum standards
for regulation in financial systems
with which every financial institution
around the world must comply.
Now, the challenge is that
these are simply agreements.
They then have to be implemented
into the individual regulatory systems
of jurisdictions of countries around the world.
The end result is that the AML,
Anti-Money Laundering requirements,
the KYC, Know Your Customer requirements
in the United States,
or Hong Kong,
are all very similar but not identical.
Now, if you are a large financial institution
which is doing business
across these major economies,
part of your regulatory requirements
are to comply with the requirements
for KYC and AML
in each jurisdiction in which you operate.
And the number of regulators,
particularly those in the United States
have put in place some very large fines,
multiple billions of US dollars
against large financial institutions
for failures to
properly have global systems
to know their customers
and prevent money laundering.
As a result,
financial institutions have had to
whereby they can at a moment’s notice
know who all of their,
in many cases,
tens of millions of customers all over the world are,
and where they’ve gotten their money,
and whether they raise any risks
of criminal use of the financial system
or terrorist financing.
And the only way
the financial institutions
have been able to do this
is spending large amounts of money
on employing people
and building technological systems
to standardise account opening processes
and the various reporting requirements
that money laundering
and KYC regulations
around the world apply.
Another area that we’ll see
is what is called MiFID II.
MiFID II is the Markets in Financial Instruments Directive
in the European Union.
And MiFID II requires transparency.
It requires disclosure of
massive amounts of information
in particular about financial institutions,
securities and derivatives trading activities.
A very good example,
Merrill Lynch in 2017 was fined
almost 45 million British pounds
for failing to report
almost 75 million transactions
over a two year period.
Now, that is
roughly 150,000 transactions a day
for every business day
over that two year period.
And the only way that
a financial institution
can possibly keep track of
that amount of trading activity is through
building automated compliance systems
to package and report
the information to regulators.
So, from the standpoint of regulators,
the incumbent financial industry,
particularly banks have so far been
a major driving force.
4.5 RegTech Eco-system: Start-ups
Predicted to be one of the fastest growing sector
of 2018, the RegTech startup ecosystem
has grown rapidly to match that expectation.
Entrepreneurs are driven
by the US$100 billion market opportunity
that represent compliance spending.
This has created a market of over 300 startups
which is fueled by
cumulative US$100 billion of investments
by VCs since 2012.
To better understand the RegTech startup ecosystem,
let’s start with some context.
With the USA setting the tempo
of global regulatory changes
and characterised by a fragmentation
of supervisory bodies,
most of compliance spending will occur there.
However, whilst the USA represents
a strong natural client,
the startup creation activity
is predominantly based in Europe.
As for Asia, the region represents
1/3 of global compliance spending
but is underserved by home-grown RegTech startups.
The 2008 financial crisis
has represented a strong catalyst
for regulatory changes across the world.
The combination of fines, over 321 billion,
regulatory changes which have tripled
in the last three years
and post-crisis reform implementation
such as Dodd-Frank or Basel III
has forced banks to increase their operating costs
as a response to their new regulatory obligation.
To just give you a sense of scale,
a financial institution like JPMorgan has added
another 14,000 legal and compliance staff since 2012.
It is not unusual for banks
to have 20 to 30% of their employees
working in compliance-related function.
This means that a tier one universal bank
has more compliance officers and lawyers
than Facebook has total employees.
However, the number of recurring fines
occurring post crisis is challenging the effectiveness.
However, the numbers of recurring fines
occurring post-crisis is challenging
the effectiveness of simply adding up
human resources to meet compliance obligation.
Indeed, for each one dollar spent on compliance,
three are being spent on regulatory fine.
It seems that the compliance industry
has difficulty to learn from its mistake.
This is what RegTech startups
are trying to address.
A decade has passed since the crisis
and since then most regulatory changes
have been implemented.
Financial institution are therefore now starting
to look at how to automate compliance obligation
and decrease the added recurring cost
that has built up post crisis.
RegTech startups are answering these demands.
RegTech companies can be classified
in three broad categories.
Each time I will quickly illustrate them.
First, regulatory compliance.
Here we are going to find companies
that are learning about regulatory changes
and telling banks how this is going to impact
their business logic.
Second, risk management.
It’s about identifying, for example,
conduct risk to prevent another Libor scandal.
Third, financial crime.
How can we understand the ultimate beneficiary
behind a shell company to avoid money laundering
or terrorism financing?
Within these three categories,
the vast majority of startups
are found in the regulatory compliance space.
This reflects the fact
that this represents a low hanging fruit for success
mainly because data is available,
it requires limited integration
and it has a lower risk factor
in case of error by a RegTech company.
Similarly to FinTech companies,
the majority of RegTech companies
are B2B providers selling to financial institutions.
Whilst demand from this client base is strong,
it appears that the sales cycle remains long.
On average, we’re talking about 12 months.
Certain exceptions are noted
especially in the context of
upcoming regulatory deadline
such as MiFID II or GDPR
which therefore fast track the sales cycles
and the procurement process.
Additionally, I would like to mention
that RegTech startups can also be found
in two other areas worth mentioning.
The B2G or business-to-government space is growing.
Regulators globally are engaging RegTech startups
via various channels from hackathons
to accelerators to find solution
to enhance the supervisory and regulatory function.
Similarly to financial institution,
regulators are driven by the cost benefit
provided by RegTech startups
that can reach a factor of up to 10 times.
These savings are actually especially important
when considering the fact that tax payers’ money
is used to finance regulators
and their operating costs
making it a strong public policy case.
However, whilst having regulators as a client
brings a lot of legitimacy,
the lengthy procurement process
is extended by additional tendering rules
relative to sourcing supplies
from the public sector.
Secondly, certain FinTech companies
are directly adding regulatory compliance process
into their product.
For example, in the context
of wealth management space,
funds products are now being sold
and marketed only to pre-qualified investors
by leveraging on their data
to determine suitability, location and investment profile.
Whilst for consumers, this provides
a level of personalization of services;
for financial institutions,
this embeds regulatory compliance
into the sales process
and that’s important
because it allows to avoid fines relative to mis-selling.
Previously what the financial crisis has highlighted
was that compliant and sales function
were operating in silos.
This overview concludes
the RegTech startups ecosystem of Module 4.
4.6 RegTech Start-ups: Challenges
Let’s talk about the very exciting topic of RegTech,
RegTech, short for regulatory technology.
But first, what is RegTech?
RegTech is the use of new technologies
to solve regulatory and compliance burdens
not only more effectively but also more efficiently,
and this has been an area that has been
really booming in recent months and years.
Why is that?
Well, since the financial crisis,
banks and other financial institutions
have hired thousands of compliance officers
or risk officers to try to help them comply with
the various regulatory requirements around the world.
Well, the good news now is we could use
some of the latest technology,
from artificial intelligence to big data analytics,
to try to tackle some of these issues
in many ways better than humans can.
Let me give you a good example,
Today, trying to stop the proceeds
from criminal organisations or rogue nations
from entering the global financial ecosystem
is a big challenge.
And, despite all our efforts,
of all the different AML procedures
and prophecies that we have in place,
the success has not always been there.
According to a recent study,
we’re only able to capture
less than 1% to 2% of laundered transactions
around the world from entering the financial ecosystem.
Well, the good news is this is hopefully
an area that RegTech can help.
We can use artificial intelligence
and actually try to actually spark
and actually improve the process of how we filter
and actually monitor these transactions.
And this is happening across
various, various, various verticals
of the broader regulatory space,
from KYC, know your customer,
and onboarding to regulatory reporting.
But although RegTech sounds very interesting,
that has also a lot of challenges.
For example, if you’re a RegTech startup
and you’re trying to sell to a bank,
the sale cycles can be very, very long.
Think about it.
You know you’re dealing with
a lot of sensitive information
and actually a lot of regulatory information,
where the downside for a bank for getting
it wrong is actually very big.
So, we actually are seeing these long sale cycles
that are common when you’re trying to sell to a bank
actually be even longer when it comes to RegTech.
Also, not everybody,
when it comes to in risk or compliance functions
are as familiar with RegTech.
While a lot of the business functions of a bank
got familiar with FinTech in the last couple of years,
we’re still in the early stages when it comes to RegTech
with people in risk, compliance, or legal functions.
But also probably more importantly,
if a bank wants to use this new technology
they need to be able to integrate this
inside of the spaghetti of their legacy systems,
all these different systems that the bank has been
implementing over the years and now try to find a way
to use this new technology to come in
and actually be able to use it.
So, there are still a lot of challenges,
but the good news is we finally
have a lot of the solutions available
to help financial institutions,
not only be more effective, but also be more efficient
when it comes to regulations.
4.7 RegTech Ecosystem: Regulators
The RegTech Ecosystem,
in addition to traditional financial institutions
also extends to regulators themselves.
Think about it for a moment.
In the context of KYC requirements,
one of the major elements
of a compliance framework,
of the regulatory framework,
is filing of what are called
suspicious transaction reports.
Any time a transaction,
in most jurisdictions,
over 10,000 US dollars is done in cash,
that financial institution will have to
file a report with the regulator.
Likewise, any time
any unusual transaction takes place
with any of a bank’s millions of clients,
a suspicious transaction report must be filed.
Now, think about this
from the standpoint of regulators
that are receiving
thousands of suspicious transaction reports
from banks every single day.
What do they do with these?
Is it simply pieces of paper
that they store in a warehouse?
that is very often been the case.
If an event would happen,
a terrorist attack
or a criminal conspiracy of some sort,
regulators would then
trawl back through those records
to see if they could identify
any additional information.
But a better system would be
regulators putting those reports
in a digital format,
which can be immediately
subjected to data analytics,
which would not only be useful
after a terrorist attack occurs,
in the context of tracing the financial flow,
but it could potentially be used
to prevent attacks
or a criminal use
of the financial system.
And that is the idea of RegTech.
Regulators themselves applying technology
to achieve better regulatory outcomes.
After all, anti-money laundering rules
are not about
producing suspicious transaction reports.
They are about
reducing the criminal or terrorist use
of the financial system.
if technology can be applied
by both the financial institutions themselves,
as well as regulators,
to better achieve that objective
that is a very important development,
and one of the biggest trends
that we’re seeing in the context of
today’s financial markets.
Now, if we look at regulators,
it is, in fact, not new for them
to use technology in regulation.
One of the longer-standing uses of technology
by regulators in the context of regulation
occurs in the context of insider trader,
Insider dealing takes place
when an insider of a company
listed on a stock exchange
buys or sells shares of that company
on the basis of information,
which is not available to the public.
Remember we saw
with the efficient market’s hypothesis
that the idea of market’s functioning
is based upon availability
and if someone is using information
to profit in markets,
which is not more widely available,
one, that is not good
for market functioning
and market trust,
but it’s also unfair to other participants.
One of the most common places or times
that insider dealing takes place
is in the context of
the announcement of a merger or acquisition.
That will typically take place
at a certain day,
a certain time,
and regulators will use
the electronic trading records
of the stock exchange,
and will look back
through six months of data
to see if there were
any unusual transactions by insiders,
like corporate directors or their families,
during the period in the run-up
to that announcement.
That could then trigger an investigation
and potentially an enforcement action.
That is RegTech.
Today if we’re looking at areas of
regulators using RegTech,
probably the three biggest that we see are,
first, in the context of
applying big data analytics techniques
to the massive amounts of regulatory filings,
which have exploded
since the 2008 global financial crisis.
Like a large financial institution
is having to make several thousand reports
to regulators around the world every day,
are receiving thousands of reports,
and are increasingly using that information,
subjecting it to data analytics,
in order to identify potential market risks
or market violations.
A related idea is
what is called macroprudential policy.
Prior to the global financial crisis,
regulators tended to focus on
the safety and soundness
of individual financial institutions.
The idea was that if you prevented
each individual bank from failing
that would prevent a financial crisis.
What the 2008 crisis showed is that
sometimes it is the interlinkages
between participants in a financial system
that causes the crisis.
It is not what one is doing,
but the fact that
everyone is doing the same thing,
and when a certain trigger event happens,
it causes problems
in the entire financial system.
Since the global financial crisis,
regulators have spent
a very significant amount of effort and resources
on coming up with ways
to try to prevent crises from happening
before they take place.
And one of those areas
is using data analytics,
in particular in the context of regulatory filings,
to try to identify risks
prior to them actually happening.
And finally, one that we’ve seen before,
As the financial system
has become increasingly digitised,
that has also increased its risks
of hacking and cybersecurity.
Perhaps the best example of this in recent years
has been the hacking of Equifax.
Equifax is a large company in the United States,
which provides credit rating services.
It collects massive amounts of data,
uses that data to provide a credit score,
which banks and other financial institutions
then use as the basis of lending decisions.
In 2017, it announced that all of its data
had been hacked
of over 140 million people.
And that highlights that,
as a financial system becomes more digitised,
the risks of cybersecurity go up.
And many would say today that
cybersecurity is not the biggest financial risk,
it’s not the biggest economic risk.
Many would, in fact, say,
that it’s the biggest national security risk as well.
Because, after all, in addition to
disrupting or stealing money,
hackers can also seek to
bring down large financial institutions
in an effort to cause
the collapse of the financial system
and the economy.
So, like financial institutions and startups,
regulators, too, are a major part
of the RegTech Ecosystem.
Payments & the Regulatory Landscape in Asia Pacific (KorumLegal)
Hello, welcome to this lecture on payment services
and the regulatory landscape for them in Asia Pacific.
I’m Danh Nguyen, the General Manager
and Managing Consultant for EMEA of KorumLegal.
Most people are familiar with
the term “financial services”.
Less is understood about “payments”.
Not many people know or understand what it means.
“Payments” has traditionally been neglected
or underserviced as an area of the law
and as a subset of general banking
and financial services.
This is out of step with how payments
have quickly evolved to become a hotspot of
commercial and FinTech activity in recent years.
In addition to the more traditional players
such as the high street banks
and credit card companies,
like MasterCard and Visa,
and global remittance players like Western Union,
new players are constantly emerging.
These include Monzo, Airwallex, TransferWise,
WorldRemit, TransferGo, Aussie Forex,
Paybase, Azimo, the list goes on.
The level of innovation
in this space is dizzying
and many hundreds of millions of dollars
are being poured into these companies
by astute investors.
So, what do we mean by payment service?
At its simplest, a payment service
is any service provided by a financial institution
to allow a person or a business to pay
another person or business
for a product or service.
A payment service provider offers merchants
online services for accepting electronic payments
by a variety of payment methods
which includes credit cards,
bank-based payments such as
direct debit, bank transfers,
and real-time bank transfers
based on online banking.
Typically, they use a Software as a Service model
and form a single payment gateway
for their clients to multiple payment methods.
Payments also cover
money transfer and remittances,
including consumer to consumer,
business to business, consumer to business,
and business to consumer,
and credit cards, debit cards,
stored-value cards, and so on.
Now that you understand better
what is a payment service,
let’s now touch on the regulatory frameworks
for payment services in the Asia Pacific region.
Until about eight years ago,
the regulatory landscape for payments in APAC
was relatively light-touch.
Regulations were playing catch-up
with the new technologies
which allowed for an abundance of
new products and services to emerge,
The existing regulatory frameworks in many countries
were simply not equipped to
address the many challenges
and risks posed by these new technologies,
products and services.
Most of these laws were enacted at a time
when these technologies did not exist
or weren’t contemplated.
For example, in Singapore,
the relevant law,
the Money Changing and Remittance Business Act,
dates from 1979 and remained largely intact
with few substantive updates
or modifications for 40 years.
The new law to replace it,
the Payment Services Act 2019,
was only gazetted in February 2019.
It’s expected to come into effect sometime
at the end of this year or early 2020.
In Australia, remittance services
and remittance service providers
were not regulated until 2011
when the Anti-Money Laundering
and Counter-Terrorism Financing Act, 2006
was substantially amended to provide for this.
Lawmakers could not have imagined
the full potential of technology
to fundamentally transform how payment services
would be provided to consumers
and businesses in the digital age.
Few could have predicted that you could send
and receive money via your online messaging app,
or with a few simple clicks on your smartphone,
you can open a bank account
or apply for a debit card.
But lawmakers and
regulators entrusted to enforce them
have been catching up.
They’ve been busy enacting new laws,
or amending existing ones,
to regulate these new activities and products.
Since 2010, we’ve seen new payments
or funds transfer laws being enacted
in Japan, Singapore, Malaysia,
Thailand, Indonesia, Australia,
New Zealand and the Philippines.
While each national law is different,
they all essentially share some common themes.
They all focus on AML/CTF compliance,
effective technology risk management,
the need for robust internal controls
and frameworks, and consumer protection.
For example, those against risks of fraud
and unauthorised use
or mishandling of customer funds.
I will now speak a little more in-depth
about one of these new laws,
the Payment Services Act in Singapore.
The stated objectives of
the Monetary Authority of Singapore,
the Central Bank of Singapore,
were to streamline payment services
under a single legislation,
enhance the scope of regulated activities
to address current and future developments
in payment services,
calibrate regulations according to the risks
the activities pose by
adopting a modular regulatory regime.
The Payment Services Act
adopts an activity-based approach
to the licensing and regulation of payment services
and classifies them under seven key activities.
You can see these on the screen right now.
More than one activity can be engaged in,
but only one licence will be
needed to cover all activities.
Retail payment activities will be grouped
into three main licence classes.
Money changing licence,
standard payment institution licence,
and major payment institution licence.
The regulation of licensees
is calibrated according to their activities
based on the risks
and these regulatory concerns.
Money laundering and terrorism financing,
consumer user protection, interoperability,
and technology risk management.
No doubt, the emergence of new technologies
has created tremendous opportunities
for new players to enter
this exciting and dynamic market.
The financial services sector
was ripe for disruption.
Consumers have greater choice
when it comes to the types and
availability of financial services products.
This has also enhanced financial inclusion
and access for everyone.
But with new opportunities,
there are additional legal
and compliance obligations and risk.
Regulatory and compliance burdens for players
seeking to operate in this space
can be quite onerous
and shouldn’t be underestimated.
Scrutiny from regulators in the region
has increased significantly.
Regulators expect payment service providers
to have in place robust compliance,
risk management and governance frameworks
to ensure that they can meet their legal
and regulatory obligations,
and to protect consumer rights and interests.
There needs to be a thoughtful approach
to compliance and risk management
which will help the company to establish
disciplined management of financial crimes,
operational risk and consumer protection.
Regulators also expect providers to
take active steps to enforce the compliance
and risk management framework,
and to monitor compliance
against the requirements.
Navigating through these complex and
onerous legal and regulatory regimes
can be daunting.
The costs of compliance can be high.
In some cases, this may affect
the commercial viability of their business.
So having in place a well-thought out strategy
and an effective and robust compliance
and risk management framework will help
the payment services providers
to take full advantage of the opportunities
that the new technologies offer.
It can also cushion the business
from the downside risks
of running a non-compliant business.
The legal, financial and reputational risks
for the company
and for its senior management team
are far too great
to simply ignore or downplay.
But where the company has a good understanding
of the regulatory requirements
and a culture of compliance is promoted
and embraced by senior management,
the company is positioned well for success
in this burgeoning area.
Industry Showcase: The Application of AI in Smart Regulation (Interview with John Craig from Mindbridge)
Well, I think regulators are looking at technologies
and startups such as MindBridge
to be able to introduce new technologies and new ideas
faster into the industry.
In particular, in our case, we’re working with
machine learning, artificial intelligence
with organisations such as the Bank of England.
And using that technology,
they’re able to better review and regulate
say the credit unions in the United Kingdom.
So they use machine learning, artificial intelligence
to be able to look at the liquidity ratios,
make sure that the banks are performing
the way they’re supposed to,
and analysing for things such as trend shock
when the bank introduces a new policy,
how are all the credit unions changing
in relation to each other
and being able to better understand
the different changes in policy that
bank makes relative to the credit unions.
I believe that regulators feel that
startups are a cost benefit
because they’re able to
accelerate the use of the technology
within their sphere of influence.
Well, here in Asia, the industry players
that are leading the charge are
groups like the Hong Kong Exchange.
And from a regulator perspective,
I certainly see, you know, strong movement from
say the Hong Kong Monetary Authority
in terms of using technology
such as artificial intelligence and machine learning
to better understand the regulatory market.
I think it’s already begun.
I think regulatory technology
has already started to become a widely used term
in the industry, and as such,
I think it’s going to only grow from here.
It’s certainly well-established in North America,
and I think it’s quickly gaining pace here in Asia.
4.8 Regulatory Sandboxes
We’re now going to talk about Regulatory Sandboxes,
a safe harbour for
supervised innovation in financial market.
The general perception of regulators
is that they are slow and reactive.
The Dodd-Frank Act illustrates this.
It was passed in 2010,
but its full implementation
remains a work in progress.
In almost ten years that have passed
since the great financial crisis,
what has emerged strongly is FinTech.
Whilst the regulators have been
implementing post-crisis reform,
entrepreneurs have been busy.
Today with the regulatory houses in order
regulators are being to also embrace
The best illustration of this is the speed
at which Regulatory Sandboxes have been announced.
So far, over 12 have been announced in the world,
out of which six have emerged in the last four months
and the majority of them are in Asia.
One might argue that
the boom in Regulatory Sandboxes
is yet another reactionary move driven by
the jurisdiction’s fear of missing out
on the chance of becoming or staying
a meaningful financial centre.
One can also argue that the intent behind establishing
a Sandbox matter as much, or more,
than their narrow regulatory outcome.
Establishing a Sandbox demonstrates
a Regulator’s desire to move forward
towards a more proportionate regulatory framework
that bounces risk with innovation.
And whilst most Regulators seems to have realised
that Sandboxes are not child’s play
many are yet to invest adequate resources
into their operation,
either from a human capital perspective
or technical capacity.
Similarly, as with startups,
Regulators will have to enter into
a reiteration cycle to improve
the quality of and add value to their Sandboxes.
Regulators will need to keep their visions alive
as Sandboxes are unlikely in the first year
to deliver much by ways of result.
Perhaps most importantly,
regulators must ensure
that in the event of another crisis
they do not fully revert from the proactive approach
that they have towards yet another reactive one.
In other words, the innovative spirit
of regulators needs to be nurtured and maintained
as it does with entrepreneurs.
Each needs to learn from the sum of their experience.
Similarly to startups,
regulators should cherish
the journey because innovation
and its effective regulation are ever evolving processes.
This is not to say that regulation
always follows innovation.
India and Europe have demonstrated how reform
can spur innovation and the rise of FinTech.
However, we need to ask the question
of how a proactive
and adaptive regulatory framework can be created.
At the core of this vision lies the numbers of
well explored and established principle for regulators.
First, financial stability at the macro and micro level.
Second, market integrity and
third, consumer protection.
The challenge is using technology to develop
a better way of doing things
from the standpoint of regulators,
the industry and infrastructure.
As an example, whilst all regulators agree
on the importance of AML and KYC,
there has been limited harmonisation
with respect to this most common form of compliance.
Too often, the spirit of the law
is distorted by its implementation.
In the context of banks, this often takes
the form of banks and regulators blaming
the other for each failing to take
the responsibility in addressing
the question of why they cannot innovate.
Lack of innovation is blamed on
a mutual misunderstanding as to what is possible
and how it can practically be achieved.
Regulatory Sandboxes should become the new form
for discussion in which the dominant outcome
is promoting innovation rather
than only trying to reduce risk.
This means that the regulator should not seek
to prevent all risk from occurring,
but instead evaluate whether
an innovation enhances or decreases risk
in comparison to what already exists.
Let’s take some examples.
First, it’s about accepting driverless cars
because they are statistically less likely
to generate an accident as opposed
to failing to support them because
they have caused one or two accidents.
Second, it’s about supporting facial recognition solution
as the instances of fraud associated with them
is likely to be far lower than
with traditional chip and pin mechanism.
For this to happen, regulators
will need data and a lot of it.
This is to ensure the true risk association
with a given innovation are adequately estimated.
Foreign experiences should also be taken
into consideration as their data are used.
The depth and size of the data necessary
has placed immediate pressure
on regulator’s resources.
With limited staff,
regulators will hit a bottleneck
in their review, evaluation,
and approval of innovation.
As a result, regulators need to start
embracing machine learning tools
which will allow them
to better understand large and unstructured data sets.
For now, the largest simulation in the world is in China.
China doesn’t need a regulatory Sandbox
because the whole country is one.
In its technology firm other kids playing in it.
When Alibaba released
its experimental credit scoring tool,
Sesame Credit, it was quickly apparent
that its credit scoring algorithm
was being gained by its users.
Sesame Credit was underpinned by the assumption
that the more a person spends or receives,
the more credit worthy they are.
However people quickly started
to send the same amount
of money back and forth in order
to increase their credit score.
The unusual patent allotted to Alibaba
and lent to the algorithm being readjusted.
However, not every country has the capacity
to run control experiment on the scale of China.
These countries will either have to rely
on machine learning tools or accept the risk
of regulation lagging behind innovation.
This is why Regulatory Sandboxes represent
a critical addition to the toolkit
of innovation available to regulators.
Industry Showcase: Balancing Innovation and Regulation Challenges in Hong Kong (Charles Mok)
Now even in the context of China,
we have seen some of the developments
get to such a stage where they’ve had to come in,
develop regulatory frameworks to balance out risks.
And this is something that I think
we’re seeing around the world
and this is the idea of piloting,
or a test-and-learn approach, regulatory sandboxes.
And this idea of sandboxes was something
that I wanted to ask you particularly about,
coming from the tech side of things, and that is,
we have these new regulatory sandboxes in finance,
but where does this idea come from?
It’s a good question.
I think some of the regulators around world
starting doing it first
but Hong Kong started looking at it
a couple of years ago, and said that particularly
because as we said in our regulatory regime,
we have to follow rules.
Some companies come out and say
I’m going to raise funds in this particular way,
I’m going to issue a cryptocurrency
in this particular way,
and it’s that violate the existing laws.
We’re not going to be able to allow them to do that,
but if that’s the case,
how do we balance the need for innovation
and giving some of these things a good try?
So we’ve been advocating that we should be trying
this particular approach. You know, for Hong Kong,
I think we have had some experience
in the last couple of years.
I hope that we would provide more incentives
for some of these companies or banks
or other institutions to try.
Initially, the regulators were a bit more conservative,
they only allowed banks to try
and then the rest of the innovators,
the startup companies, said well, then what do I do?
I have to partner with a bank?
Which means that in some cases
this is exactly the reason
why they want to do what they want to do
is to not to work with the banks.
So there have been some struggles in the beginning.
An added problem for Hong Kong would be the facts
that we have three different regulators,
for banks, for insurance, and for securities,
so which means that sometimes
these regulatory sandbox environment
would be a little bit more complicated.
So those are being worked on and
I think the regulatory sandbox experience is improving,
but I just came back from Silicon Valley,
having visited some of the companies over there,
including some of the startup companies
in FinTech over there.
I think what we need is a bit more promotion,
because that’s also important.
Sometimes they say
hey, we didn’t know that Hong Kong,
you have this particular environment
that we could give it a try.
But I’ve heard of other economists or countries saying
that they have this regulatory sandbox.
So I think now that we have something
that might be might be good,
we’d better give it the right promotion
to make sure that more people
around the world know about it,
because after all, one of the most important things
about FinTech that I believe, is that the nature of it
is going to be quite global.
And also that’s one of the advantages
that Hong Kong has
is being a traditional financial services centre.
We have the expertise, we have the banking
and the financial service expertise in Hong Kong,
not to mention the technology expertise.
And so we should leverage this particular advantage
to attract more startups or people around the world
to say hey, maybe I should give my idea a good try
in Hong Kong.
Yeah, I very much agree with that.
4.9 Smart Regulation
We suggest that RegTech goes beyond
simple use of technology
for compliance or regulatory purposes
and also extends to cooperative efforts
between policymakers, regulators, startups,
and traditional financial institutions
to build better financial systems.
We call this the idea of smart regulation.
Smart regulation suggests that
in the context of today’s technological transformation
of the financial system,
it is possible for the first time
to redesign underlying infrastructure,
the plumbing of the global financial system
to make the financial system
work better and effectively,
The idea of this is first,
a need for better information and monitoring
that regulators need not only
to follow what is going on
in traditional financial institutions
but they also need to be aware of new entrants,
whether those are FinTech startups,
and also evolution of technologies.
Whether those technologies
are things like cryptocurrencies,
blockchain, cloud techniques
or anything more that emerges
in coming years.
So the first stage is really
an understanding of what is going on.
Without an understanding,
a regulator cannot do a proper job
of balancing the objectives of
economic growth and financial stability.
From that basis,
one can work together
to design better systems.
And really, the starting point in smart regulation
is systems design.
It is the idea of digitising regulation
so that regulatory requirements
to the greatest extent possible
can be conducted or met by industry participants
in a digital form.
Digitising reporting requirements
and other compliance requirements
allows financial institutions to submit reporting
and other obligations to
regulators in a digitised form.
That process of digitization
immediately makes it more straightforward
for regulators as well as the industry
to apply processes of data analytics,
in order not only to
better achieve regulatory objectives,
but also to reduce costs
and increase efficiencies
as well as discover new opportunities.
The idea of smart regulation
is that technology is no longer the limiting factor
in how a financial system or its regulation works.
But to take advantage of these new opportunities,
we must be aware of
what the technology can do
as well as the existing inefficiencies
in many of our systems,
and that is a RegTech process
of designing financial infrastructure.
4.10 Redesigning Financial Intrastructure: India Stack
This idea of designing better financial infrastructure
is core to the idea of FinTech and RegTech’s
re-conceptualization and reconsideration,
recreation of both domestic
and global financial systems.
We can see this happening
in two big examples.
The first is one that
we’ve already looked at in the EU.
The development of a series of
new legal frameworks
reflected in MiFID II, PSD2, and GDPR,
which are transforming the way
that the financial system
in the European Union works.
The second is in the context of India.
And something that we’ve seen
throughout the course so far
is that China is probably the most advanced,
the most exciting digital transformation
that we’ve seen so far in the context
of this FinTech environment,
but the other transformation
which is taking place now
is in many ways just as exciting,
and that is the digital transformation
that is taking place in India.
In India about eight years ago,
a group of tech entrepreneurs
set about designing a strategy
to build a new system of digital infrastructure
to transform not only the Indian financial system
but access to finance
by the majority of the Indian population
and also India’s economy
and economic growth prospects more generally.
In some ways, this was partially a reaction
to the success that we’ve seen
in China’s long-term economic transformation
since the late 1970s.
The idea in India is a series of interlinked pieces
called India Stack.
The foundation of the India Stack
is a new digital biometric ID system.
That digital biometric ID system
includes 10 fingerprints
and two iris scans
for each individual who is issued with an ID.
And since 2010,
over 1.2 billion people in India
have been issued with new digital IDs.
That is more than the iPhone sold
in the same period from its launch in 2007.
The digital IDs in India
are the largest IT rollout
in the history of the world.
Well, that’s a good start.
Digital identity to make it possible
for immediate authentication
of any individual’s identity.
The second level has been a system built on
an electronic payment system that is open,
which means that it is open
not only to traditional banks,
but also to new entrants.
This payment system allows digital payments
to be made from one ID holder
to another very rapidly.
It also allows the entrance of
new forms of business,
whether those are payment providers
or robo-advisory services
or P2P lending and others.
The third level involves
increasing the use of bank accounts.
And India three years ago,
only about 1/3 of its population
would have had a bank account,
roughly 350 million people.
Now, as a result of a process
whereby government salaries are paid into accounts,
pensions and other benefits,
agricultural supports and the like
are all paid into individual bank accounts,
there has been an explosion
in the number of bank accounts in India.
Over the past five years,
over 300 million new bank accounts
have been opened in India,
more than the population of the United States,
and to the point where now for the first time,
over half of the population of India
has a bank account,
a bank account that is linked to their digital ID
and to an electronic payment system
that allows instantaneous payments
across the financial system.
The final element of this system
brings us back to the ideas of KYC.
An e-KYC system, an e-KYC utility
where individuals have
a sort of Dropbox-like account
where they can place digitised documents
which are the necessary filing requirements
for KYC requirements
across a range of financial institutions.
As a result of this combination of factors,
India has not only exploded the number of people
using bank accounts
and using the financial system,
it has also dramatically reduced the time taken
and the cost for opening an account
and as an added benefit
dramatically reduced the cost of corruption
in a previously cash-based economy.
In November 2016,
the Indian government announced
an incredible experiment
what was called demonetization.
The government said that
86% of the cash bills in circulation in the country
First, we’re going to be talking to you as an individual.
How is GDPR in Europe
covering and impacting businesses
as well as you as an individual?
What about digital identity?
How a piece of password can now be condensed
in a piece of data,
and how that piece of data
can better define who you are,
and therefore customise experiences around you.
We’ll then be also looking
at the financial services industry.
They have been using data since the 1970s,
and have been able to deliver new financial products
on the back of this.
However, there is challenge on doing so at scale.
Startups and the FinTech transformation
is also one which is underlied by the use
of technology and data accessibility
both by technology companies
as well as the financial institutions.
On the technology company side,
we’ll be talking about the challenges
brought by TechFin companies,
as well as the governments of AI.
And finally, regulators.
The increased value of data means
that more and more people want to
access it by legal and illegal means.
And therefore, the regulation of data
from a data privacy perspective,
or the protection of data
from a technological perspective,
will also be addressed in this module.
Module 5 Learning Objectives
Module 5 discusses data, data regulation, data security and the emergence of TechFin. In focuses on both the opportunities as well as the risks and challenges arising from the process of digitization and datafication in finance, as well as policy and regulatory approaches and their implications for business.
In Module 5, learners will:
Understand the role of data in financial services
Consider various approaches to data protection and privacy
Think about the challenges of digitization and datafication, particularly cybersecurity and technological risk
Discuss the emergence of TechFins and the implications for financial services
5.2 History of Data Regulation
When looking at data
and how it has been regulated over time,
there are different elements to it.
First, we can approach data from a privacy perspective.
A lot of you can relate to this
when we look at data breaches that infect
your personal information being released to the public.
What has been happening with Facebook
and how that data, for example,
has been used in the context of political elections,
both in the US and in Europe.
Data privacy matters
because it’s all about protecting your personal life,
and that life is increasingly now online.
The second element is about
cross-border data management,
and whilst digitization is a global phenomenon,
cross-border data management
is not necessarily something
which is as automatic than one may think,
and there’s some right reason to do so.
Certain jurisdictions are, for example,
much more stringent about data residency rules,
where data can only be held and stored
in a single country and not leave it.
This is one of the big problems for FinTech companies.
Whilst FinTech companies have global ambition,
data regulation, especially on how
cross-border data management is done,
can be a real limiting factor for their ambition of growth.
Why? Because for example, a regulator in a Country A
may not want that the data of its consumer
transferred to Country B where it will be processed
and then the insight brought back to Country A.
This can be for many different reasons,
including national sovereignty and national security.
Since 2007, the financial crisis has shown
that financial services stability
is a national sovereignty issue, and as a result,
data residency and data protection is equally important.
The third element is data management.
This is how people will access and control their data.
One of the topic that, for example,
we’ve covered in Module 4 was GDPR,
and here it’s about this idea that
every single piece of data
should be able to be traced back
to its individual owner, if it identified him.
And if it does, that owner should have the right
of querying the institution who has seen that data,
how many times the data has been used,
and how the data is actually being used,
and in which countries.
This is the idea of data that could be accounted
as a liability or an asset, from a financial perspective.
However, from all perspective,
data and its regulation
should be looked from another angle.
It should be looked from an angle
where data equals money,
and all the rights and obligation attributed to it
should be similar to money.
Data would therefore have a property right,
a commercial right,
and that can be transferred, exchanged, borrowed,
or leased to someone else.
And this matters a lot.
It matters because it will allow digital identity
and data sovereignty to keep on growing as an industry.
It also matters because as the economy
is going from a service economy to a digital economy,
more and more individuals will be looking at ways
of monetising their data to supplement,
if not totally replace their income.
So far, this topic of data regulation hasn’t emerged yet
because the money that can be made
from data monetisation
has not passed the poverty line.
However, when this will happen,
a real question will be raised
at the regulatory and the policy level
on how that data regulation should be approached.
From a trend perspective,
we can imagine that this will mainly
happen in countries like Asia.
This is because, proportionally,
the value of data from an individual in Asia
is higher compared to their salary
to what you would have in Europe.
For example, someone in Vietnam
earning 8,000 US dollar a year,
but having their data worth 50 US dollar a year,
has a higher value as a proportion
of their data to their salary,
compared to a European person
earning 50,000 US dollars a year,
but only having 50 US dollar worth of data.
This is why the future of data regulation
and data being treated as money
is mainly going to come from places like Asia.
However, so far, the regulatory tempo
has clearly been set by Europe,
which has one of the most stringent and complete
regulatory regime around data management,
which is followed across the world and including Asia.
5.3 Data in Financial Services
The importance of data in the economy
has been captured by Professor Klaus Schwab,
executive chairman of the World Economic Forum.
His analysis entails that the world
is entering a fourth industrial revolution
powered by artificial intelligence
and being distinguished
in its characteristic of hyper scalability,
which we will discuss more in module 6.
Importantly, the fourth industrial revolution
has been built on top of the third era,
which started in the 1970s,
thanks to computer and automation.
As we have seen throughout this course,
and financial market development
have a close relationship.
Professor Schwab’s third era
of computer and automation coincides
with the digitization of financial services,
which we discussed in module three.
And in this lecture, we are instead
projecting ourself forward,
aiming to answer the following questions.
How can the data created since the 1970s
be used meaningfully in financial services?
And what does the fourth industrial revolution look like
for financial markets?
The importance lies in the fact
that as with any large economic shift,
the relationship of money
and power is being redistributed.
As such, answering if financial institutions need
to become data companies matters.
Should their business model move
towards a data refining instead
of interest or fee-based income?
In order to find answers,
we will present three examples
in exploring in turns, big data,
artificial intelligence, and analytics tools.
Let’s start with the hedge funds industry.
They have historically been distinguishing themselves
by their capacity of using quantitative skills
and hiring a lot of Math scientists.
They have increasingly been leveraging
an external and new alternative data
to make better investment decisions.
For example, the use of geolocation data
was used to estimate the footfall
in shopping malls, and therefore predict
the growth of sales ahead of public quarterly reports,
allowing to make an investment decision
before public information is out.
In the context of the insurance industry,
we have seen usage-based insurance changing
the current flat-fee insurance model
and instead moving towards
a pay-as-you-go service
centred around your lifestyle.
This has been made possible by the increase
of telematic solutions,
such as trackers in your car,
or your mobile phone,
which is then analysed for better pricing.
Here, data is being used
to change the underwriting decision model
whilst becoming the most cost-effective option
for you as a customer.
Finally, in banking,
by using predictive analytics,
a bank is able to monitor the spending patterns
of a consumer, and then
infer if a larger purchase will be made.
The size of that purchase will then define whether
the bank should proactively propose
a consumer loan or mortgage.
Likewise, a very simple example
is taking a travel insurance as soon as
you buy a plane ticket with your credit card.
Now, these examples illustrate that data
has previously been used in financial services,
but this has not been equally done by all.
This is mainly due to the fact
that data today remains a by-product
of activity as opposed to a core asset that is valued.
To make this change across the industry,
new C-level roles are emerging,
such as Chief Data Officer.
Regulators are also concerned by this,
and you saw that in module 4
when we were covering the topic of smart regulation
and what it means from a human capital perspective
for the regulators.
Now, in order to enhance the value of data,
whether internal or external,
going forward, financial institution will need
to consider the three Vs of data.
The combination of behaviour tracking
and Internet of Things is increasing
the amount of data points available.
The speed at which data is created requires
the need for real time analytics,
as it challenges the current storage capacity.
The ability to handle,
understand structured and unstructured data
from internal or external sources.
As this transformation process occurs,
FinTech startups are increasingly providing
the necessary analytical tools to process
and analyse data held by financial institutions.
For now, these remain narrow use cases
and include identification of credit risk
in loan portfolio, transaction of quarterly reports
into investment advice,
or performing complete audit risk review.
Whilst these have always been performed by banks,
today the combination of data availability
and analytical tools make it possible
to do these in seconds instead of hours,
and this is done without a trade-off on accuracy levels.
Hopefully, this will provide banks a capacity
to restore profitability levels,
whilst for consumer,
create a new era of invisible finance,
one that support your lifestyle instead
of creating friction as you’re trying
to achieve your goals.
Industry Showcase: Application of Data Analytics in Finance (vPhrase)
Hello everyone, I’m Neerav Parekh.
I’m the founder of a company called vPhrase.
We’re from India.
I’ll tell you about how AI
is being used in the data analytics space
for FinTech companies.
So, we created a product that a lot of institutions use
for improving their report.
Let me give you a few examples.
Motilal Oswal which is one of the largest brokerages,
they use a platform for creating portfolio analysis
for their investors,
so we examine 500,000 portfolios every month
and create statements for the investors.
The whole idea is to give them
the key insights in the portfolio, using language.
So, AI is being used to generate language from data
to explain all the insights to the leader.
So, that is one example.
Another large very large private bank in India
is using our platform to create personalised reports
for the branch managers,
so the whole performance of the branch,
all the data is taken in,
is analysed and language is written
to explain to the branch manager
how exactly is this branch doing.
So, a few bullet points we’ll tell him,
Ok if your branch’s achieving its targets,
which products that are doing well,
which products that are not doing well and so on.
Then there’s another large investment bank
which is using our platform
for risk analysis of stocks and mutual funds.
So, they study the performance of mutual funds,
they study the performance of companies
and we use that data to create risk analysis reports
for them on those stocks and mutual funds.
So, as you’ll see,
we’re using AI for data analysis
and then creating language,
natural language generation,
so that people can understand the data better.
Enterprises can make their reports
easier to understand for their people.
Industry Showcase (NEW): How Startups are Transforming the Asset Management Landscape in Hong Kong? (MioTech)
Hi, my name’s Jason Tu
and I am the CEO and Co-Founder of MioTech.
We are a startup for financial institutions
to better manage and draw insights into their data.
Our company is two years old
and we began this journey in Hong Kong,
the largest financial hub here in Asia.
Just how important is this market?
In 2018, the Hong Kong Stock Exchange
actually snatched the crown jewels of IPO listings,
raising more than US$36.6 billion for 208 companies.
Hong Kong’s Asset and Wealth Management Business
reached more than US$3 trillion Asset
on the management
based on SFC’s annual survey in 2017,
effectively making Hong Kong the No. 1 leader in Asia.
This is just to give you a gist of how important
financial services is to Hong Kong, and vice versa.
Now let’s take a look at the ecosystem
surrounding Asset Management.
For every trade, from the back office perspective,
it will go through custodians, exchange,
clearing and settlement agencies,
and possibly, fund administrators
until accurate information is reflected
in Asset Manager’s hands.
From a front office perspective,
for every investment opportunity,
the asset manager
needs to gather conventional data sets
such as prices, volumes, as well as,
unconventional data sets
such as news, and alternative data.
Asset Managers are at the top of the pyramid,
they digest all these information
in order to deliver return
and avoid risks on their financial assets.
So, where are the opportunities in this industry
for a startup
and particularly in Hong Kong?
This goes back to Hong Kong’s unique role
as the gateway to both China
and broadly speaking, Asia.
From the data and technology perspective,
the market data
and market intelligence industry in Hong Kong
is still dominated by
western market data providers,
with a lack of coverage
and understanding of the local markets.
These large multi-national players
also have decades of company history
and very large complex corporate structure
that don’t allow them to adopt new technologies
as fast as startups can.
The lack of data coverage on Asia
and slow innovation on the technology side
creates opportunities for disrupters to fill the gap.
From a product and user experience perspective,
the entire financial software industry
is lagged behind the consumer app
and Software-As-A-Service industry.
From Google, Facebook to Slack, Zendesk,
user centric design and browser based enterprise
applications have completely changed
both the consumer and enterprise world.
There is a huge opportunity to create a product
that adapts to the modern user
as compared to having clients to adapt
to decades-old financial software
that was created
before most of the younger traders were born.
The question is how?
There are a number of tips
I can offer to entrepreneurs
aiming to change the asset management industry.
First of all,
Hong Kong has unparalleled advantage
in its access to the Greater China Region
and the rest of Asia.
However, entrepreneurs should keep in mind
that understanding Hong Kong isn’t enough,
but rather, you should be eager
to learn the needs in Greater China and Asia,
the places where Hong Kong has access to,
that’s where your users are.
Second of all,
the asset management industry
already has a very complex business structure
compared to other industries.
We have to be very careful
about business model innovations,
and not to introduce more complexity.
Technology innovation, on the other hand,
has much more room to improve efficiency
and reduce complexity.
Last but not the least,
as I’m speaking here right now,
talent is the single most important factor
I’m hopeful that Hong Kong will attract
more technology talents to mix up
with these finance professionals
to disrupt and to make a difference together.
Industry Showcase (NEW): Digitisation of Financial Services – A DBS Approach (Parts 1) (DBS)
Disclaimer: Views expressed are personal and do not reflect the views or thoughts of any organization the speaker may be affiliated/associated with.
Ladies and gentlemen, just to set the scene
and introduce myself.
I’m Mark Li
and I run a digital program office
for transaction banking in DBS.
So I’m really happy to be invited
by the University of Hong Kong
to talk about the digitalisation of financial services,
So my first question to all of you is:
Why does a traditional bank need to go digital?
Why can’t traditional banks just stay
within their comfort zone
and run their business as usual?
The reason is pretty simple.
Just to stay competitive.
In recent years, we have been
experiencing gigantic changes
brought by internet companies
such as Tencent, Alibaba, Amazon, etc.
They are opening up
their own affiliated financial subsidiaries
to engage in businesses that traditionally banks
are doing for a really long time ago.
We also see an increasing number of
startup companies that are focusing on
a single banking function and customer segment.
Within this, you can always find
that they may do a better job
than banks in terms of customer experience
and product offering.
Cost is another reason.
By putting up brick-and-mortar data centre in the clouds,
banks will be able to enjoy a lower cost
while expanding the banking business.
I’m not here to promote any banks
or any digital aspiration
but there are a few things I wish I could share
which I have been strongly inspired
by the top management in DBS.
Once our CEO Piyush has said,
“DBS is not a bank anymore.
Our competitors are not our fellow friends
in our banking industry anymore.
Our competitors are what I mentioned just now,
Alibaba, Amazon and Google.
We have to think, act and transform
like a technology company.”
That’s why DBS is a 27,000 startup.
How does this bank transform themselves?
In short, the entire backend is 85% cloud.
Yes, it reduces cost, improve resilience and scalability.
Across Asia, they have more than 180 APIs
and 60 API partners.
And their connectivity for client ranges
from all kinds such as transacts,
FX enquiries and ecosystems.
And that they have a philosophy and practise
that digitalise the entire internal process,
increases productivity, joy space
(which is work without any fixed space)
that promotes collaboration across units,
space improvement and optimization.
This is what I believe the basics of a new digital bank.
And GANDALF, of course, it is quite a funny thing,
as all of you may have watched
“The Lord of The Rings and the Hobbit”,
GANDALF over there is very different from
the DBS GANDALF.
Here it means Google, Apple,
Netflix, I’ll put the D aside,
Amazon, LinkedIn and Facebook.
All of them are technology bigwigs
who have reinvented their products and ways of working
to suit the rapid technology advancements
and yes, this is a digital bank.
And of course, the D over here,
I hope it means DBS and of course, the digital bank.
This shows a bank aspiration to become one of
the disruptors that can change and rock the markets.
And most importantly, it is not only a slogan.
It means a fundamental culture change.
A shift from projects to products and delivery.
Developing high performing agile teams
rather than just project management,
automating everything, yes, everything.
Designing for modern systems
means simple deployment,
organising for success and most importantly,
adopting all of these habits of large innovation
by providing fresh and new user experience.
The best thing of it is they don’t work in the box
but focuses at client deliverables,
such as co-creating solutions alongside of clients,
practise business agile,
solving real-life problems,
and that is digital.
In consumer banking group,
we have APIs that promotes
instant credit card promotion from transaction banking
from where I come from, of course,
with corporate insurance firms communicating
and transacting with us through APIs.
For treasury and markets,
we quote rates and transact real time on FX
with clients through APIs.
Even from a system perspective,
we have created a blockchain community
with suppliers and anchors
to promote a better trade environment.
As you can see from these three examples,
we are fully automated from top to bottom.
From client interface to back-end processing.
From client’s connectivity to internal dot-and-node.
My experience of DBS, this bank,
the Digital Bank of Singapore, we always joke,
you can really feel a huge change and difference
in people’s mindsets.
The way they work, the way things are structured.
This 27,000 startup
is so willing to experiment
and practise with the customers
and one thing I’m really amazed
is that doing business reviews
or even day-to-day meetings,
we do not use PowerPoints or Excels.
We do not create meeting decks
but instead, we use dashboards and data
for business decision.
We are hugely obsessed with data.
Data is our DNA and data is our basis
for business decision.
Many examples I can quote in the business environment
how data is being utilised to justify investments,
our lending principles,
collaboration across business groups or data driven.
Traditional banks like DBS
can actually be a FinTech ourselves.
Transform ourselves into a digital bank
which embraces many qualities below.
Digital management and staff culture,
Cloud-based and API-based technology
across all kinds of clients’ footsteps,
open to external clients’ connectivity
and most importantly, they’re able to accept failure
and adopt to changes very, very quickly.
Industry Showcase (NEW): Digitisation of Financial Services – A DBS Approach (Part 2) (DBS)
Disclaimer: Views expressed are personal and do not reflect the views or thoughts of any organization the speaker may be affiliated/associated with.
Another critical part of digital transformation
is to understand the legacy issues we are facing
we not only for DBS but also as a bank as a whole
and we have to improve with a digital mindset.
A few legacy examples, physical cash for all of us today
who really comes up without physical cash
in Hong Kong and Asia?
Let’s talk about branch needs for banks,
whether you agree or not, there’s still a strong demand
for physical branches in Hong Kong and in Asia
what kind of services
really needs to be done in branches?
Or can we move in digital,
even in terms of transaction and transparency?
Why can our fellow friends in the FinTech market
such as TNG or Tap & Go
offer instant overseas remittance at very low costs,
but many traditional banks cannot?
Account and mortgage opening at least 4 to 8 weeks
if not even able to complete the entire process
not to mention it is hugely paper-based
for majority of banks in Hong Kong,
if not even the region,
of course, one thing on emerging customers as well
in the way they interface with the buyers,
can they rely on bank to offer
an omni-channel experience,
or they need to have many devices
with different turnaround times and agreements?
For customer information retrieval,
why does bank data need to be retrieved from
internet banking but not embedded into daily apps?
These exactly are many legacy issues
that I can make one simple conclusion:
We need to offer
a brand new digital experience to our customers.
Change management is hard
and transformation comes with
both by time, disruption and experience.
And bank in a Hong kong example,
by collaborating with HKMA, HKICL,
fellow banking industry, SVFs and corporate customers,
we are trying to incorporate Faster Payments
and various SVF payments into our daily lives
in different progress.
The same for account opening,
there are many elements over here,
one is CDD (Client Due Diligence) and KYC (Know Your Customer) process
and the other is internal setup within banks ourselves.
For the former,
I truly do not see how this can change much over time.
So let’s talk about the latter,
this can be improved
by a fully automated and digitalised process
that’s within the bank’s back end,
instant data interchange of credit bureaus,
collaborating with RegTechs,
and of course with support from the government
in creating a digital, retail and corporate identity record.
The greatest difference across virtual banks
versus traditional banks is that
many VBs claim they can provide
a better customer journey
and customer experience to our customers.
My question again is: Why can’t traditional banks do so?
In 2025, I believe for banks to succeed,
they cannot exist alone.
They cannot offer lending standalone
offers standalone internet banking platforms,
and of course maintaining competitive for customers
to transact or even to sell insurance,
banks need to stay connected, like our customers’ life
for example, in DBS’ ambition of “Live More Bank Less”,
having invisible banking,
or providing banking service in an ecosystem
or together with partners in the world or in Hong Kong,
I believe this is the key for future.
I believe this is the new era
where banking service can be invisible,
in behind of daily lives or business services,
without going to detail into the process,
there are few ways traditional banks can actually help
to be a FinTech ourselves
and participate in these ecosystems build up
by participating in partners’ platforms
so that we can engage with the customers
across partners’ customers journeys,
or by creating platforms alone
that connects third parties’ platforms together
and of course creating an ecosystem
of course limited by the bank’s size and strength.
This is a dilemma over here:
I believe banks need to be
as inclusive into our customers daily lives if possible,
on the other hand, I do not believe a bank
can never and will never be able to create interfaces
that covers all segments,
it must be through ecosystem partnership,
it must be through enabling with FinTech partners,
it must be through collaboration
with many other financial institutions
in an automated manner
embracing collaboration over competition.
So thank you ladies and gentlemen,
I hope by sharing right now
of how a bank can actually work as a FinTech
and also collaborate with other FinTechs
can drive more change and momentum
in a market for Hong Kong and for Asia
to be more digital and more innovative.
5.4: European Big-Bang: PSD2 / GDPR / Mifid2
We’ve seen across this course
how technology has transformed finance,
and in particular, we’ve seen how finance
has transformed finance in China,
perhaps more so than any other place in the world,
how firms such as Ali Baba and Tencent
and their financial subsidiaries like
Ant Financial and WeBank
have led a digital transformation,
driving finance in China
from a cash-based, bank-based system
to one which is based largely on data and technology,
digital forms of payment,
digital forms of lending,
digital forms of investment,
all based upon techniques of automation,
big data, and increasingly, artificial intelligence
to build a framework
for an entirely new 21st-century form of finance.
We’ve also seen in the context of India
how India has been seeking
to build a digital infrastructure
to support the transformation of its financial system.
Likewise, in the European Union,
a series of changes coming into effect in 2018
are fundamentally transforming the relationship
between finance and data, setting the stage
for a new European financial system
not based on monetization of finance,
but rather on monetization of data.
And these three pieces go by
the short names of GDPR, PSD2, and MiFID II,
three rather uninteresting strings of letters,
but fundamentally transforming the way
that finance and data relate in the context of the EU.
GDPR is the General Data Protection Regulation,
and this has evolved out of a long process of building
a legal framework for privacy
and data protection in the EU.
GDPR is fundamentally about giving individuals power
and control over their individual personal data.
It allows individuals to direct
holders of their personal data
to delete it, forget it, transfer it,
and in any way that it is the data of the individual,
rather than the data of the company, of the firm.
And this is fundamentally transforming the way
that businesses think about their relationship
to their customer and their data.
This in particular is a big challenge for tech firms
like Google or Facebook or Ali Baba or Tencent.
Another second area is what is called
the Payment Services Directive 2.
PSD2 sets new rules to allow open entry of a wide range
of new entrants to the payments industry.
But beyond allowing
an increasing range of new entrants
and encouraging digitization
and transparency of payments,
PSD2 also creates a series
of requirements for open banking.
In other words, for a customer,
if a bank has your data,
information about your financial history,
your financial transactions, your financial accounts,
PSD2 requires those banks,
if you so direct, to allow
third parties access to your data, to your accounts.
And this fundamentally sets
a framework for banks’ business
being transformed as customers’ data and access
to their accounts is no longer under the control
of banks and other financial institutions,
but it is open to new entrants,
new startups, new tech firms,
all of which are gaining access to massive
amounts of data under the control
of the individual consumer,
rather than the financial institution.
And finally, MiFID II.
MiFID II is
the Markets in Financial Instruments Directive II.
MiFID II is an initiative that has evolved
out of the 2008 global financial crisis.
It is fundamentally about creating greater transparency
in the formal financial markets,
markets for bonds, shares, derivatives, and the like,
whether those are traded on exchange or off exchange.
And amongst many other things,
it requires the reporting of all transactions
relating to EU financial instruments,
like bonds or shares or derivatives, whether or not
those transactions take place on or off exchange.
In terms of total size, it has grown to be
more than 1.7 million paragraphs in length,
7,000 printed pages of regulations,
which have required financial institutions to spend
literally tens of billions of dollars in
building new systems
to meet its reporting requirements.
Each of these systems,
PSD2, GDPR, and MiFID II,
were all designed for specific reasons.
But no one thought about what would happen
when you combined them together.
And this will be a big bang
transforming over the coming decade
the European financial services industry,
bringing together data and finance in a way
that has never been seen before in the context
of the EU single financial market.
Industry Showcase: PSD2: Open Banking API for Startups (Gini)
For decades, FinTech startups
have been developing useful apps
and products to supplement normal banking products,
and also give consumers more control and insight
into their own finances.
However, banks have never been keen on sharing data,
so FinTech companies have had
to spend considerable resources
to develop workarounds to get the data.
One of the main ways they have done this
is through screen scraping.
Screen scraping is the act of developing a programme
to crawl through a website
and copy the information into another database.
This is commonly found in search engines like Google,
which send crawlers through the internet
to develop their search engine database.
This year the European Union
implemented Revised Directive
on Payment Services aka PSD2
which included regulations to promote the development
and use of online and mobile payments
such as open banking.
Other countries are following suit,
and in Asia, both Hong Kong and Singapore,
as well as other countries
are exploring opening banking APIs.
Opening banking APIs would facilitate
the growth of FinTech companies
and also give consumers greater choice
and opportunities in managing their financial lives.
Industry Showcase: Methods of Data Protection: GDPR Compliance and Personal Privacy (Exate Technology)
Hello, my name is Jonathan Naismith
and I am the Business Manager for Exate Technology,
a RegTech firm specialising in
data protection and data privacy,
enabling regulatory compliance
with the EU General Data Protection Regulation
as well as Cross-Border Data Transfers.
The recent data breaches at firms
such as Equifax, Uber and Facebook’s breach of privacy
have led many to question the ability of firms
to protect sensitive client data.
Sadly, these recent breaches
have become more of a trend than a phenomenon
as they are increasingly common.
So, why is this the case?
To date, data protection has typically been done
on an application by application level
and each application is protected by an IT developer.
The common problems with this
are your IT developers are often contractors,
and thus probably work somewhere else now
and they view the task of protecting those applications
as boring and time consuming.
With new computing viruses being created daily
and the introduction of
quantum computing nearly upon us,
traditional data protection has become outdated
and needs to be addressed now.
At Exate, we encrypt or tokenise the data
on an attribute by attribute level at rest,
in transit, and in memory as required under GDPR.
This then allows an organisation
to separate application security from data security.
The question now becomes,
“How does one view the data if it is always encrypted?”
Exate ensure that data is seen strictly
on a need to know basis for
that specific individual, team, department or firm
to fulfil their role.
We achieve this by providing
your data with a virtual visa.
This is done by wrapping metadata or rules
around each individual data attribute
and allowing those attributes
to flow throughout the organisation with those rules.
Exate then just sit outside a firm’s applications
and acts as an automated border control for your data.
To explain, imagine you have flown into an airport.
If you are a local resident,
you go down the fast track,
that is your public data, it doesn’t need to be protected
it just flows throughout your organisation.
Now your tourists,
they have to go to the man in the booth
who checks their passports and looks at their visa
before deciding whether or not
they can enter the country,
we do the exact same thing for your data.
So just before the data enters an application
we look at the rules around each attribute
and match that back to the individual
who’s trying to access the data.
Now one of two things can happen.
One, the individual passed the rule check
in which the data will decrypt, or two,
if the individual fails even one of the rule checks,
the data will remain encrypted
and their access blocked.
Exate then provides clients
with immutable forensic proof
of who accessed, or attempted to access what data.
In addition, Exate are able to provide customizable
user reports on your data,
in short, data about your data.
Lastly, Exate Technology has
no access to your clients’ data,
their decryption keys or their applications
and requires no code changes to integrate
with any web-based application.
It is important to note
the same technology can be used for
Cross-Border Data Transfers.
I hope this has been of interest to you
and I would like to thank Hong Kong University
for allowing Exate to share this with you.
5.5 Digital Identity
This module will be around digital identity.
The reason why digital identity matters is because
it will be one of the most impactful technology
in the next five years
for you as a viewer.
Identity can be defined
in four different types of categories.
Physical identity will be
elements such as your fingerprint,
your iris, or your DNA.
Legal identity will be, for example, your passport,
your Hong Kong ID, or your driving licence.
Physical and Legal identities
is what we call static identity.
However, you also have something called
For example, your electronic identity
is your social media.
Facebook, Twitter, Weibo and WeChat
are example of social media
that are covering your identity.
And finally, behavioral identity.
The way you talk, the way you walk,
the way you type a message
is very unique to you.
And therefore, electronic and behavioral identity
are typically called dynamic identities.
Now let’s bring that in context of finance.
When you’re a financial institution,
you will typically get to know your customer
by the time they going to register,
by giving their passport, or their legal identity,
and electronically when they
will be transacting with you.
What you buy,
the insurance products that you take,
or the mortgages that you actually get from the bank.
Apart from these touch points,
the bank doesn’t necessarily know you.
They don’t really have an access
to your behavioral identity,
they don’t really have access
to your electronic identity.
Therefore, the banks,
unless you often are transacting with the bank,
and if you have a bank account in the first place,
will typically be unable
to have a full picture about yourself.
And this is why for example,
in the West we call thin credit files.
These are people that,
whilst banks would often have financial interaction,
and therefore are not always recorded
as a customer of that bank.
Now in that context, identity actually has few flaws.
In the West, identity is more and more unsafe.
Look at the recent data hack
that has been happening for example in Equifax.
Equifax was holding financial information,
and now one in two Americans
has information exposed to the public.
However, if for example,
in the context of developing markets,
people don’t necessarily have passports.
Because 1.2 billion people in developing countries
do not have a form of formal identity,
they cannot typically enter financial service industry,
and therefore, we need to totally rethink
the way we’re going to be identifying people
to authenticate a transaction.
So let’s take some of these examples.
First of all, we’ll go in India.
India has launched a programme called Aadhaar,
which is now providing 99% of the population over 18,
with a 12-digit number
that is essentially a representation
of your biometric data and your geographical data.
With your Aadhaar number,
not only can you withdraw cash at an ATM,
but you will now also be able to authorise
a large transaction,
or even do an online learning course
by scanning your iris to identify yourself
as a student of that course.
In Nigeria, digital identity is being used
for civil servants.
This is very important for the government
because they have realised
that there is a lot of identity theft coming on.
That identity theft is then leading to people
either receiving two times a salary,
or receiving someone else’s salary.
And since implementing digital identity to civil servant,
they have now saved over 75 million US dollar.
In the context of Europe,
you have PSD2 on one side
and GDPR on the other side,
which are changing the way
data of consumer is being used.
Whilst PSD2 is more about payment competition,
and whilst GDPR is about personal data protection,
the combination of both
is putting the individual
at the centre of the financial services industry,
and empowering them with their own data.
Therefore, now someone would be able to give access
to their bank account to a merchant that actually
directly wants to transact,
bypassing the traditional card network,
or the merchant acquirers.
Therefore, what we see is that
finance is being changed,
and digital identity alongside.
We need to go from a model
where we trade data for convenience,
to a world where we trade data for compensation.
Part of that step is going to be GDPR in Europe.
GDPR, while not allowing individual to
monetize their data,
is providing individual control on their data
by notifying an individual of a breach,
or by allowing an individual of doing data sharing.
Finally, the next step is going to be about reforming
and how ownership is done.
Once we have covered the ownership part,
we’ll have to consolidate our data into a single wallet.
That aggregation will allow us to bring together
the behavioural data, the electronic data,
the legal data, and the physical data
into a single wallet that we will control,
and then we can either grant access to,
or monetize from.
This is how digital identity will impact your life,
and now let’s have a guest lecture
that will share slightly more details.
5.6 Change in mindset: Regulation 1.0 to 2.0 (KYC to KYD)
This RecTech Module has shown you
how regulation is changing.
This change of regulation
can be captured by the notion of KYC to KYD.
Know your customer to know your data.
The reason why this is changing,
is that from a regulator perspective,
we’re going from a world where we’re regulating people
to a world where we’ll be regulating processes,
automation, and algorithm.
And this requires a totally different mindset
but also skillsets for a regulator
to perform that job when that transition has happened.
One of the first things that we’ll see
will be about human capital.
First, regulator from a human capital perspective
will bring brand new stuff in to
better perform their role as regulators.
And in particular I have in mind data scientists.
Just like technology companies or financial institutions
have gone for the last few decades,
regulators will keep on
increasing their team of data scientists.
The second part of that is
that from a mindset perspective
regulators have to accept that technological neutrality
is not anymore a starting point.
What’s technological neutrality has been
a very important notion for the regulators to prevent
them from chasing the latest changes in technology,
today this is not sufficient anymore.
The reason why I say that
is because you have learned in the first module
how financial technology cycles are shorter and shorter.
But that’s exactly the same thing for regulators.
Think about it.
From your personal perspective as a consumer,
a few years ago you would use your pin code to
authorise a transaction.
Last year, you might have used your fingerprint
and tomorrow you might be using your iris.
From a regulator perspective
if every single time that technology are changing
and evolving and you go to them and you say
please can you find me an authorised technology,
the regulators will be bombarded
and clogged with the amount of requests coming in.
And therefore what they said is
I do not care about the technology,
tell me about what you’re trying to achieve
from a process perspective
and I will make sure that process is complied with.
The problem with that approach today
is that more and more of the decision making
is automated as well as the origination of products,
and therefore if regulators don’t understand
how algorithms are operating at the code level
and the type of data that they are using,
it will be very very difficult
for them to perform their role
which is about consumer protection,
and even competition.
Regulation is therefore changing.
We’re going to go from
regulation 1.0 to regulation 2.0.
And to do that transition from
a know your customer world
to a know your data paradigm,
a few changes will have to happen.
We have put together seven key points on
how regulation is done today and
how it will be done tomorrow.
First point, consumer protection.
What is important for regulators
to ensure that the money of people
doesn’t get lost or misappropriated?
I think that this quote has highlighted to you
the increasing value of data
either from a monetization perspective
or for a financial decision perspective.
And therefore, data protection and data privacy will
be equally important as monetary protection of
your individual deposits.
The second point is prudential regulation
which requires firms to put control in place as well as
capital in order to mitigate for risk.
However, going forward,
it’s more going to be about algorithm compliance.
Where a regulator will have to do due diligence on
a system and an algorithm before you’re allowed
to go to market instead of simply asking for capital
to be put against risk.
The third point is financial stability.
While financial stability is very important,
it fails to encapsulate the notion that
financial systems are very much dynamic networks
of many individuals and companies
operating at the same time
that can impact each other.
And therefore the capacity
of regulating a financial network as opposed to
promoting financial stability will be more valuable.
And this is very much aligned with the vision
and the quote of Andy Haldane when he talks about
that Star Trek chair to supervise markets.
The fourth point is about preventing bad behaviour.
Whilst conduct risk has been a major focus point
following the financial crisis,
and this has been a positive development,
firms are still able to pay their way out
of reprehensible actions.
Therefore, regulators would set in place a system where
they can promote good behaviours in order to reinforce
the quality of financial networks.
The fifth point is about reactiveness.
Regulators especially following the crisis,
have been very reactive to the shock that
the great financial crisis has brought to them.
However, what we have seen in this course is that
technology keeps on changing faster and faster.
And therefore the capacity of being reactive to
technological change will lessen in terms of value.
Therefore, regulators need to be forward looking
and think about how finances
will be transformed tomorrow
so that they can start to prepare today.
The sixth point is about reporting.
Push compliance is when financial institutions
are sending reports to the regulators
about specific questions
that has been asked about them.
For example, for the very first time the Bank of England
has been asking financial institution to include
the impact of FinTech in their business.
What that means is that now financial institutions
are thinking twice about how financial technology is
going to be impacting their business
from a risk perspective.
Now, that simple question has therefore
changed the behaviour of the firm that
you are trying to supervise.
Therefore a better way would be
the notion of API compliance
or the capacity of regulators to pull the information
directly from the financial institution
without asking them a question and
therefore directly analyse the impact of external shocks
such as FinTech on their businesses.
Therefore, pull compliance will allow regulators
to supervise firms without
changing the behaviour of these institutions.
The seventh point is about the barriers to entry.
Today, financial markets are controlled
from the supply of institution
by the numbers of the licences issued by regulators.
You can only call yourself a bank
if you receive a banking licence.
However, tomorrow the next barrier to entry will be
about the quality of the algorithm that is held
either by the financial institution or
by the tech company.
The reason why I say that is that finance
on the back of the amount of data currently gathered
is going to be incredibly personalised just to you.
And therefore that level of customization and
individualization will represent
an experience barrier to entry which is
driven by algorithm as opposed to
simply the ownership of a regulatory licence.
5.7 AI and Governance
In a previous module,
I have briefly described artificial intelligence or AI
as part of the ABCD technologies driving FinTech.
As institutions increasingly explore
and implement more AI and machine learning
into their processes and offerings
to customers and clients,
there’s an increasing concern
from policy makers, researchers and regulators
about the responsibility and governance frameworks
that should be in place.
AI researchers have coined a useful acronym
to better understand the core concerns.
They call it AI F.A.T.
Fairness, Accountability and Transparency.
Machine learning involves algorithmic models
being trained using datasets.
However, the saying goes,
garbage in, garbage out.
Unconscious bias can arise,
including egregious examples such as image data sets
not facially recognising African Americans,
or categorising them as gorillas.
Malicious and mischievous actors
can also influence machine learning.
This was painfully demonstrated
when Microsoft’s Tay chatbot
devolved into racist and sexist rants
as a result of learning from
deliberately offensive behaviour of Twitter trolls.
Furthermore, as ever more data sources become
available to track both individual online behaviour
as well as offline activities
through connected devices
such as wearables and smart homes,
machine learning is being used
to convert these behavioural data
into individual profiles for predictive analytics.
Yet, how fair is it if machines increasingly make
financial access and pricing decisions
based on individual predispositions
rather than actions?
Machine learning is being used for important processes
such as credit ratings, search engines, bank loans,
university applications, and health insurance,
and becomes even more crucial
when applied to RegTech
which could lead to civil liability
and loss of one’s liberty.
For example, was there unconscious racial bias
in the COMPAS algorithm for criminal sentencing
risk assessments in the United States?
Do Facebook’s algorithms have a confirmation bias
that allows fake news to be targeted,
and in turn did their postings
impact the outcome of the Presidential elections?
The need to combat negative bias outcomes,
unconscious or otherwise,
remains an important source of concern.
New Challenges of AI and Machine Learning
Artificial intelligence is only so strong
and so good as the data that you serve it.
And actually when you think from
a regulation perspective,
it’s not so much the regulation
of one algorithm which is going to matter.
It’s more going to be about the quality of algorithm,
and the natural tendency of
algorithm to create a oligopoly.
Data is fueling algorithm,
and algorithm only grow by having more data.
And therefore, technology companies
are doing whatever it takes them to be able
to capture all the data they can on their consumer
to have a dominant market position.
I think that data is the new oil as a sentence
is reflecting what you had in the U.S. when you had oil.
The antitrust movement in the U.S. that was trying
to break away the big U.S. cartels around oil
is what’s going to be happening in the data space.
Because data is naturally driving network effects,
and because network effects are themselves
very naturally oligopolistic,
the regulators will need to find a way of
breaking away the dominance
of large tech firms that are controlling most of the data.
Your social data is most likely controlled by Facebook.
Your e-commerce data is more
likely controlled by Amazon.
Your professional data
is more likely controlled by LinkedIn.
These people have all the information about you.
And the only way to break away that stranglehold
on your personal data is by allowing you as an individual
to share your data to other people.
So that you can then fuel the algorithm
of a competitor with the data
that you have been building
about yourself for the last 10 years.
This is where data regulation will go in the future.
Data regulation will focus on individual data
as opposed to the algorithm adjustment
that will be required to be made
by technology companies
as requested by the regulators.
5.9 Data, Metadata and Differential Privacy
I’m now going to be talking about the difference
between data and metadata
and how this is being expressed
with the concept of differential privacy.
If you think about what financial technology is,
financial technology is about the digitization of money
but if you introduce the notion of TechFin,
TechFin is about the monetization of data.
Therefore the point is going to be
how are we are going to differentiate our data?
Currently, the way this has been done
is that technology companies
have tried as much as possible not to have
personal identifiable information about you.
The reason why they try to avoid this
is because as soon as they have PII data,
they start to be subject
to more stringent regulatory supervision
and therefore increase costs for their business models.
And so far this has worked.
The reason is because personal information
such as your age but also the content
of a discussion is something that computer
had a very hard time to understand and make sense of.
Only very recently do computer have
the sufficient computing power
as well as the understanding of the human language
to find out content in a conversation.
Instead, what computers have done
is to look at metadata.
Metadata is contextual data
which is around a content.
For example, the metadata
around my discussion with you
would be how long it took me
to actually record that video,
the content and the numbers of people
which are seeing that video, etc.
The contextual data when it’s
sufficiently cross referenced
with a lot of different dataset
which typically tech companies have
will allow you to infer content.
Let me take another example.
If I am on LinkedIn and suddenly I start
changing my behaviour, contacting more people
and asking for meeting requests,
this is going to flag LinkedIn
that my behaviour is changing
and therefore I may be looking for another job.
And yet at no point did LinkedIn
actually start looking into my messages
saying I am looking for another job opportunity.
They were able to infer that content using metadata.
Data versus metadata is
a very important thing
because financial institution
have very good at protecting your data
but technology companies
have been very good at monetizing your metadata.
Therefore we need to start to see
how we are going to create a framework
to regulate that.
And here you have an example of
what technology companies such as Apple are doing.
There’s a notion called differential privacy.
Differential privacy is about giving
the information to the person
without revealing the whole information set.
There’re many use cases that
you can relate to.
In the context of finance,
typically for financial institution to on board
a financial citizen will mean
more stringent regulatory and compliance requirements
because of that American citizen
now being on boarded.
And therefore sometimes it’s important
for a financial institution
to know that a customer is not an American citizen.
Differential privacy allows for that.
It’s about telling someone who you’re not
without revealing the whole information.
Therefore, as long as a financial institution knows
I am not an American,
I may be any other nationalities
and that’s good enough.
The other way of looking at it is for example,
accessing certain website.
Certain website will require you to confirm
that you’re not under 18
but they do not care whether you are 21 or 38.
The fact that you are 21 or 38
actually starts to be a hint
towards personal identifiable information
but that can be linked back to you
and therefore differential privacy
gets away with that notion by not sharing
the full picture by telling who you are not.
And as long as the rule set is strong in place
with the financial institution,
they’ll be able to leverage on that information.
This is what differential privacy is bringing
and differential privacy
is typically being led by companies
which are not here to make money from your data.
Apple revenue is at 80% driven by hardware sale.
Therefore, for them to not release
and monetize your data is not actually a compromise.
It’s something that makes economic sense.
But a company like Google
and Amazon or Facebook
which have much more difficulty
of actually enforcing differential privacy
because their business is about sharing
the whole complete set of information.
Therefore going forward
you will have a split.
The companies which are not monetizing your data
will support differential privacy
versus the companies which are monetizing your data
will typically look at data sovereignty
as a governance model for their customer.
5.10 Data is the New Oil: Risk of Breach
Welcome back to the HKU FinTech course.
This module is going to be about data.
Recently you may have heard about the sentence
data is the new oil.
These simple words have two deep impacts.
On the first side, it means that data
is going to be one of the most valuable commodity
of the 21st century.
On the second side, it also means that regulators
are going to have to focus on how data
is being consumed, extracted and monetized
by individuals and companies.
Now, data is not something new
and data has been around for the last few decades
and whilst two thousand represented
rising massive tech companies
which we still see today
such as the GAFAs,
Google, Amazon, Facebook and Apple,
those tech companies have approached data
in a total different way.
For example, you may have heard about the expression,
if it’s free, you’re the product.
And that sentence really means one thing.
Is that the personal data that you generate
is more valuable to the company
and this is why they’re giving you a free product.
Tech companies have understood that
and because data is core to their business,
they typically have structured data,
data that is searchable, indexable
and that can create value more easily.
But that’s not the case for everyone.
Financial institution, for them
data was a by-product of their business.
Financial institution make money
by putting a lender and a borrower together
by investing your money into the stock market
but they don’t necessarily make money by your data
and therefore the data you provide them,
whether it’s your passport
or whether it’s transactional data
is very much a by-product of their own business
and therefore that data is so far unstructured.
The difference between
unstructured data and structured data
is that structured data is searchable, indexable
and can be more easily monetized
for financial institution or tech companies.
Now, data is being used in different ways in finance.
For example, it can be used for decision making,
new business discovery,
enhancement of productivity
and regulatory compliance.
Let’s take an example for each of these.
Startups are typically improving their product
at a rapid rate because they use the data
on their product and their consumer
to keep on improving the quality of services
they keep delivering to you.
The second one is banks.
Banks are using data
to do better decision making.
For example credit scoring.
Credit score will impact
whether a bank will originate a loan to you
and if so, at what interest rate.
Regulators and specifically securities regulators
have been using data for a long time
to monitor market
and check if there’s no insider trading happening.
For example, Alibaba has understood
that there’s a correlation
between people wearing skinny jeans
and breaking phones.
The reason is because of the lack of pockets.
And therefore Alibaba is gradually starting
to sell insurance product for phone coverage
to the people buying skinny jeans
on their e-commerce platform.
In other words, what you have is that data
has many opportunities
and data will be allowing the rise of invisible banking.
For the last 100 years,
banks have changed the way they were operating.
70 years ago the notion of community bank
where you used to know your banker
and have customised service just for you happened
but it was inefficient.
And then the bank consolidation happened
and now we have universal banks
where you’re only one of a million of a customer
across the globe
but tomorrow you will have invisible banks.
Invisible bank will create individualization
of financial product just for you
even though you’re part of
a large financial conglomerate.
On the back of those opportunities
you also have risks.
Those risk are very much the factor
that data is so valuable
and the people controlling data
will now control markets.
And therefore regulators have to understand
how this is going to be changing in the future.
The regulation of data
might be one of the most important part
for regulators to focus on
as opposed to the regulation of algorithm
because we control as individual or data,
the firms control the algorithm
and it might be easier to control data access
than data output.
Finally, market reforms.
The rest of this course will show you
how regulators from around the world,
technology companies as well as financial institution
are approaching the question of data.
Whilst data ownership is very much looked
for the individual perspective,
we’ll discover that in India,
data is regarded as a public good
and therefore regulated as such.
The rest of the course will show you
all you need to know about data,
how it’s impacting finance,
infrastructure and regulators around the world.
Industry Showcase: Cybersecurity Industry Update (Microsoft)
Hi, my name is Jason Lau
and I’m the Cybersecurity Advisor at Microsoft.
Today I would like to talk to you briefly
and give you an update
on cybersecurity industry as a whole
and also some trends in the marketplace
which certainly also impact companies in FinTech.
Many say 2017 was the year of ransomware
and 2018-2019 are the years of
compliance, regulation, and privacy.
This is surely the case with new industry regulations
and laws coming in to effect this year
like GDPR, Singapore Cybersecurity Law,
PCI DSS 3.2 and many more.
“Businesses and users are going to embrace technology
only if they can trust it.”
This message is from Microsoft CEO,
emphasises the importance of trust
whenever we deal with cybersecurity matters.
The world is changing.
And in the past it used to be
it is not a matter of IF you will get hacked,
it’s a matter of WHEN.
Now hackers get more advanced
and organisations are rushing to keep up to date.
And the new phrase in industry
is that you need to assume
you already have been breached.
Former FBI Director James Comey
best summarised this as
“there are two kinds of companies,
the ones who have been hacked
and those who don’t know that they have been hacked.”
McKenzie also acknowledges this
stating that cybersecurity is a CEO issue.
And the loss of productivity and growth
is over $3 trillion.
The average cost of data breaches is $3.5 million.
And as you can see, cyber threats
are a material risk to your organisation.
Here, I’ll briefly go through what we call
the cybersecurity known attack playbook.
On average, a hacker typically sits your network
for over 500 days before they are detected.
The hacker will spend a significant amount of time
achieving their objectives,
which includes reconnaissance,
and involves social engineering
to learn more about your organisation
and users’ behaviour.
Then they decide on a strategy
for their initial compromise.
Once they are in, they will protect themselves
from any basic and typical actions companies will do,
such as rebooting machines.
From there, they will start to look
for high-privileged accounts.
And if they cannot,
they will do their best to perform credential theft
with the goal of elevation of privileges.
From here, they will persist in your network
and move laterally
and then access sensitive data.
As you can see, from a typical defender perspective,
if you don’t have measures,
security and real-time monitoring in place throughout,
you are just playing catch up with incidence response
at the very end of the lifecycle.
This is really often too late.
Thus, on top of the minds of
chief information security officers,
companies have identified
their threat detection and response,
data protection and identity
and access management
are key focus areas for 2017 and 2018.
Here I’d like to talk to you
about the evolution of security perimeters.
In the past, we would build on our on-premise network.
We would build it like a castle.
And it’d be very hard for hackers to breach
your physical infrastructure.
Then it evolved to be more of a network infrastructure.
And we got smarter.
We put in things like intrusion detection systems,
intrusion prevention systems,
smart firewalls, honeypots, etc.
But now this is not enough.
And we’re moving to what’s called
an identify driven security perimeter.
In the past, all of your files and assets
would be within your physical network perimeter.
Each day, hundreds of thousands of attempts
will be made on your network.
And it would only take one user
on the link to click through.
Often it is through a phishing email
in someone in your company,
then they will get tricked
into clicking that particular link.
The link happens to be malicious
and they could perform one of many actions.
One action could be to download and install
to gain access to your company resources.
As you can see in the diagram,
in the modern workplace,
employees access data
from work, home, cafes, hotels, airports,
and the attack perimeter now extends farther
and going to users wherever they go,
thus, giving rise to the title
identify driven security perimeter.
One way to help strengthen security
is through multi-factor authentication.
Unfortunately, all three factors
of something you have,
something that you know,
something that you are,
have all been hacked.
The trend now is to look at multi-factor authentication
and add additional layer of security
called risk-based conditional access,
where real-time monitoring
and the users’ behaviours and location
can help us to assign a user and session risk score,
which then allows us to take immediate
real-time actions to either allow access,
deny access, or request additional levels
of authentication before they continue.
Microsoft has been leading the way with cybersecurity
and trust and fighting for users’ privacy.
Microsoft has stated that cybersecurity
is a No. 1 priority for the company
and investing over one billion each year
including most recently,
the purchase of an Israeli company called Hexadite,
This paper puts forth a regulatory roadmap for understanding and addressing the increasing role of AI in finance, focusing on human responsibility and suggesting that an effective path forward involves regulatory approaches in “putting the human in the loop” in enhancing governance and addressing “blackbox” issues.