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FinTech Disruption:

A splash or a ripple

Session 11: Challenges to Avoid Financial Technology Disruption

Enoch CH’NG

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Former Associate Professor of Information Systems (Education)

Enoch CH'NG

Former Associate Professor of

Information Systems (Education)

Master of Science in Aeronautics

and Astronautics, Massachusetts

Institute of Technology

Enoch holds several appointments. He has been a non-

executive director of Citibank Singapore Pte Ltd since 2015.

He also advises Data Science Rex Pte Ltd and Hackwagon

Academy. He is also a director of AsiaMetrics Pte Ltd.

Enoch was the Director of SMU Master of IT in Business

Program (Financial Technology) until Jun 2017. During his

tenure as the program director, he gathered likeminded

students to form the SMU FinTech Club. Before joining SMU,

he was a Managing Director in DBS Bank Corporate Office.

His mandate includes the review and transformation of

support infrastructure for the global financial markets

business in DBS.

Up until Mar 2006, Enoch was the Executive Director of the

Specialist Risk Department (SRD) in the Monetary Authority

of Singapore (MAS). He represented the MAS in the Bank for

International Settlements (BIS) Committee on Payment and

Settlement Systems (CPSS). He also served as a resource

person to the MAS Board Risk Committee and attended its

meetings regularly.

Prior to joining MAS in 1998, Enoch held several key

appointments in risk management and IT functions during his

10-year tenure at UBS AG. He was a Director of UBS

(Futures) Pte Ltd, and as Alternate Director, UBS Data

Systems Pte Ltd.

Before his career in finance, Enoch worked for the Civil

Aviation Authority of Singapore (CAAS) for 9 years. He

represented Singapore at the International Civil Aviation

Organization’s Special Committee on Future Air Navigation

Systems and at ad hoc international meetings to establish

Area Navigation routes in the Asia/Pacific Region.

Enoch is keen in various voluntary works. He just completed

his third term as a member of Singapore’s National Trust

Council that promotes the adoption of comprehensive risk

management framework to reduce fraud in online transaction.

He was the President of MAS Recreation Club (2002-04) and

founding member of both the MIT Club of Singapore and

Princeton Alumni Association of Singapore. He also served in

Princeton Alumni Schools Council and MIT Educational

Council.

Enoch holds a Master’s degree in Aeronautics and

Astronautics from the Massachusetts Institute of Technology.

He graduated from Princeton University with a Bachelor’s

degree in Mechanical and Aerospace Engineering.

Core Public Debt Management Activities

Financing budget needs

Designing debt management

strategyDebt recording

Timely servicing of

debt liabilities

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How the activities may be organized

Front Office

• Borrowing and investment

Middle Office

• Debt management policy and risk analytics

Back Office

• Recording, monitoring and servicing public debt liabilities

Et Cetera

• Examples: International relations, Legal, HR, …

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Debt Management Information System

• A robust, well-functioning, and user-friendly system allows governments to:• Keep an accurate and up-to-date debt database;

• Improve work flow processes;

• Reduce operational risk;

• Produce consolidated reports; and

• Facilitate core analyses. In brief, a well-functioning and efficient DMIS enables and strengthens a debt management program.

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What is FinTech?

• Fintech is a portmanteau of financial technology that describes an emerging financial services sector in the 21st century.

• Originally, the term applied to technology applied to the back-end of established consumer and trade financial institutions.

• The term has expanded to include any technological innovation in the financial sector, including innovations in financial literacy and education, retail banking, investment and even crypto-currencies like bitcoin.

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8 Categories

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Payments

Insurance

Planning

Lending ^ Crowdfunding

Blockchain

Trading & Investments

Data & Analytics

Security

Innovative Business Models

• Typically offer one or more specific financial products or services in an automated fashion through the use of the internet.

• Unbundle the different financial services traditionally offered by service providers -- incumbent banks, brokers or investment managers.

• For example, equity crowdfunding platforms intermediate share placements; peer-to-peer lending platforms intermediate or sell loans; robo-advisers provide automated investment advice; and social trading platforms offer brokerage and investing services.

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Surprise Quiz

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Alibaba, Tencent and Baidu are 3 technology giants in China.

1. Which of their subsidiaries run their FinTech business?2. What are their valuations and number of users?

Disruptions

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• Size of circle shows estimated valuation.

• Black circle indicates funds raised (valuation not available.)

Hmmm … Time to start a FinTech

specializing in PDM?

NewGen Technologies

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Underlying Technologies

• Blockchain and Distributed ledger technologies (DLT)

• Availability, accessibility of data and analytics• Big data gets even bigger,• Alternative data

• Cognitive computing, machine learning, artificial intelligence

• Cloud computing• Storage, computing power

• Design thinking needs to be combined with creative engineering to bring the UX/CX vision to reality.

• Others• APIs• DevOps• Security technology• Mobile technology• Human Digital Interfaces• Quantum Computing• Technology talents

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What is blockchain?

A

Public,

Permanent,

Append-only

Distributed

Ledger

• A blockchain provides a digitally signed time series of data or records, put together as blocks with the linkage also digitally signed, thereby making it hard to tamper with.

• The Bitcoin blockchain uses a consensus mechanism (“mining” based and referred to as “proof of work”) to validate and authorize new information added to the ledger.

• The distributed nature of the Bitcoin blockchain through the use of blocks and hashes (alleviate the need for a central counterparty or central database), combined with the consensus mechanism (overcoming the trust problem that characterizes the internet and any networks between unknon, distributed parties.)

• A block is added to the chain of prior transactions only if such is validated.

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Distributed Ledger Technologies (DLT)

• A distributed ledger is a consensus of replicated, shared, and synchronized digital data geographically spread across multiple sites, countries, and/or institutions.

• DLT are technologies used to implement distributed ledgers.• Include blockchain technologies and shared ledgers.

• Permissioned DLT • present an opportunity to save costs if they are used to replace legacy systems and

associated back office processes, and• could potentially present a risk if, e.g. they are used to disintermediate financial

institutions and central counter-parties.

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Degree of Centralization

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Other related developments

Consensus algorithm

ToeknizationSmart

contracts

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6-layer Model for a Blockchain System

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1.Data layer

2. Network layer

3. Consensus layer

4. Incentive layer

5. Contract layer

6. Application layer

RippleNet: Payment in any currency

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3 ways big data is changing financial trading

• Leveraging big data analytics in financial models.

• Real time analytics

• Machine learning

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Trading & Investment Tools

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Structured and Unstructured Data

• Data from execution venues • MarketAxess, Tradeweb, and Bloomberg, ICAP, BGC, and Trad-X.• Price, news and more.

• Innovations in data exist in both the pre- and post-trade sides.• Filtering, mining, and then interpreting unstructured data.

• Green Key Technologies, Symphony, and Bloomberg Vault facilitate storing and transcribing of voice, chat, and email communications.

• The storage and analysis is critical for both trade reconstruction and to satisfy compliance recordkeeping requirements and other regulatory audit trail requirements.

• Market abuse monitoring and surveillance program requirements stemming from Market Abuse Regulation.

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Areas of Focus in AI

• Reasoning, problem solving

• Knowledge representation

• Planning

• Natural language processing

• Perception

• Motion and manipulation

• Social intelligence

• Creativity

• General intelligence

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Volume, Variety, Velocity, Viability and Value

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If this is true, why can’t we find MH370?

Trading Platforms

Trading Protocols

• Session Trading

• Real-tTme Matching

• End of Session Matching

• E-RFQ/ Auction

• Electronic Request for Quote (‟E-RFQ”)

• Dutch Auction

• Additional Trading Protocols

• Lit Order Book/ Quote Streaming/ Click to Trade (‟LOB/ QS/ CtT”)

• Hidden Order Book (‟HOB”)

• Dark Pool

Connectivity

• Open-Standard API Access

• FIX APIs has enabled the rationalization of sell-side connectivity by creating a common technology stack for access to markets across multiple asset classes.

• Execution Management Systems (EMS)

• Provide workflow integration between external platforms and in-house systems.

• Enable the buy-side to connect to and interact with multiple platforms and liquidity pools, thus re-aggregating the fragmented liquidity sources into a virtual market, viewable on a single EMS screen.

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Risks common to different types of platforms

Risk of conducting general solicitation/unlicensed activities.

Disclosure risks.

Cross-border risk.

Risk of collapse, fraud or malpractice by the platform.

Risk of fraud by the users of the platform.

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Other risk considerations

• Macro environment

• Adoption of new systems

• Competitive environment

• Transparency

• Liquidity

• Performance during stressed market conditions

• Product diversity

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Singapore Experience

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Project Ubin

Phase 1: Domestic inter-bank payments using a central bank issued SGD equivalent

• MAS partnered BAML, Credit Suisse, DBS Bank, HSBC, J.P. Morgan, Mitsubishi UFJ Financial Group, OCBC Bank, R3, SGX, UOB Bank, and BCS Information Systems on a proof-of-concept project to conduct inter-bank payments using blockchain technology. (16 Nov 2016)

• Successful completion was announced on 9 Mar 2017.

• Deloitte was commissioned to produce a report that covers the aspects of DLT that are most suited to settlement systems and details the design principles used for the prototype.

Phase 2

• MAS and ABS announced on 5 Oct 2017 that the successful development of software prototypes of 3 different models for decentralised inter-bank payment and settlements with liquidity savings mechanisms.

• 11 financial institutions and five technology partners.• BAML, Citi, Credit Suisse, DBS Bank Ltd, HSBC Limited, J.P. Morgan,

Mitsubishi UFJ Financial Group, OCBC Bank, SGX, Stan Chart Bank, and UOB Bank.

• Accenture was appointed to manage and develop the prototypes. R3, IBM, and ConsenSys were engaged to provide support on the respective DLT platforms of Corda, Hyperledger Fabric and Quorum. Microsoft was engaged to support the deployment of the prototypes on Azure Blockchain.

• MAS and ABS announced on 14 Nov 2017 that the report has been published and the source-codes and technical documentations have been released for public access under Apache License, Version 2.0.

• Central banks, financial institutions, as well as academic and research institutions are encouraged to tap on the open source-codes to facilitate their experiments, research and innovation.

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Ubin ‘Spin-off’ Projects

• First project• Driven by SGX focuses on making the fixed income securities trading and

settlement cycle more efficient through DLT.

• Second project• Focuses on new methods to conduct cross border payments using central

bank digital currency.

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The Singapore FinTech Festival

• Drew in more than 13,000 participants from 60 countries

• Components include:

• Global Fintech Hackcelerator

• Fintech Awards

• Fintech Conference

• Investor Summit

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Concluding Remarks

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Impacts: Opportunities & Risks

‘Customer experience’

Efficiency

Credit risk Compliance risk

Operational risk Data

Unknown risk

• The technology is scaling at an exponential rate.

• Increasing complexity and data generation is transforming the way we work.• A unique opportunity for processing,

analysing and leveraging the information in useful ways. Machine learning and algorithms are increasingly being used in financial trading to compute vast quantities of data and make predictions and decisions that humans just do not have the capacity for.

• It is more than just a flash

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Challenges

Technological Challenges

• Scalability

• Interoperability

• Cyber resilience

Operational challenges

• Governance

• Smart contracts

• KYC/AML

• Legal

Trading and settlement related challenges

• Management of the cash leg of transactions

• Recourse mechanism

• Position and collateral netting

• Transparency

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Do you remember?

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• What is the technica l debt we are incurr ing?• Wil l AI replace me?

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