FINTECH SEMINAR SERIES

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(from left to right: Mr. Andrew Lim, Mr. Alan Cheung, Mr. Terry Chan, Prof. Raymond Tsang)

Introduction/Background

In this panel, leaders from the regulator, commercial bank and technology research institute explored the fintech-banking collaboration on the use of open API. HKMA has been a long time advocator of smart banking initiatives, especially in the area of Open Application Programming Interface (API).  Hang Seng Bank (China) has leveraged on Open API to facilitate collaboration with fintech companies in the mainland; while ASTRI has also developed secured portals for third party service providers to access clients’ bank systems with client authorization.

Mr. Terry Chan, Senior Manager of HKMA who is a seasoned professional in promoting the development of Hong Kong’s fintech ecosystem; Mr. Alan Chung, Senior Director of ASTRI who drives fintech and smart city initiatives; and Mr. Andrew Lim, Head of IT of Hang Seng China who oversees all IT functions of the bank, discussed how regulators could provide an environment for fintech to be developed in a way that balances risks and returns; how open API could be applied to facilitate the use of alternative credits for SMEs; as well as how a bank can leverage open API to facilitate cross-border data transmission.

Balancing risk while fostering an open and innovative habitat

Terry highlighted HKMA’s key towards balancing both lies in two elements of the Open API Framework: high-level guiding principles, and non-compulsive. On one hand, the framework provides core principles and assumptions both banks and service vendors should have in mind when developing open API initiatives; but on the other hand, it is not HKMA’s intention to mandate their implementation as it may limit innovation in the industry and might hinder the development of best practices.

Open API, Machine Learning, Credit Score and Privacy

Alan used alternative credit scoring for SMEs combined with machine learning as a use case of open API. Alternative data, both transactional (e.g. cashflow data) and non-transactional (e.g. external reports and behavioral traits), can be extracted and analyzed from various sources to form an alternate credit score to facilitate credit applications. Federated machine learning, a distributed middle ground between machine learning and data privacy, enhances the entire process by meeting the needs on both functional and privacy ends.

Cross-border expertise

Using Hang Seng Bank as an example, Andrew highlighted how 3rd party vendors help navigate cross-border open API concerns. The biggest pain point the industry faces is the need to comply with mainland data regulations, and the expertise of 3rd party vendors proved to be a solution to the challenge. China’s PIPL (personal information protection law) is quite stringent on how cross-border data can be stored, processed and transferred, but ensuring the compliance with such rules is quintessential to capturing business opportunities in mainland China. By collaborating with an experienced 3rd party vendor in China, Hang Seng is able to overcome such challenges as the vendor’s rich experience in similar areas allowed them to develop infrastructures that are fully regulations-compliant, while managing costs and a timely delivery.

Q&A highlights: futureproofing young talents

When asked to give some tips for the younger generation to get into the industry of fintech, Andrew encouraged young people to develop a solid understanding of both Chinese and western technology. Under the influence of geopolitical tides, China is increasingly pushing the replacement of western technology products (e.g. Cisco, Microsoft Office) with home-made alternatives (e.g. Huawei, WPS Office). Such trend is expected to cause the technology scene to diverge into two streams: the east and the west; and hence, the mastery of both is a key to futureproof one’s skillset in the upcoming decades.

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Introduction -   How to make reading data less terrible – a beginner's guide to leveraging financial data for decision making

In today’s increasingly volatile and complex economic environment and erratic market factors, how can we stay on top of the market and make better decisions? Many people turn to data studies for a solution. It may seem like data is available to us at our fingertips, but paradoxically to some people, it is challenging as they face problems such as data lacking in quality and structure as well as data standardization. All these prevent us from making decisions based on data efficiently and effectively. In this seminar, the speaker, Miss Emily Chan, who is a Buy-side Solution Specialist and leads the APAC news advocacy strategy in Bloomberg, made use of three case studies to demonstrate how better data use, research and analytics, can potentially enhance financial market transparency, and subsequently increase the effectiveness and efficiency in our decision making.

Key takeaway from the seminar

Conventional wisdoms

By comparing different ways of presentation on the Hang Seng Index, Emily highlighted how the way we present data affects the validity of the data. The tools and ways of visualizing data will directly affects the insight we draw from data, therefore, a well-developed tool is necessary when conducting data analysis. Emily introduced Bloomberg’s research and analysis tools as an example, in which it helps users to identify the products that are aligned with investment objectives, and hence assist users to conduct detailed analysis and drive effective and efficient decision making.  

Market sentiments 

Macro market sentiments is the general prevailing attitude of investors to the anticipated price movements in the market. However, with high volume and time sensitivity and dependence of news and social media stories, it is difficult to extract actionable information with human eyes. Therefore, use of automatic tools is necessary. Through the sharing of how Bloomberg users monitor social media polls on a Bloomberg terminal from influential market participants, or key topics affecting the investment universe, Emily demonstrated how we can extract useful information and systematically quantify market sentiment within a short time, and hence drive efficient decision making.  

Trend analysis

Besides reading historical data and live data, prediction from data is also one of the key components when making decision. Emily took interest rate as an example, and demonstrated how AI algorithms, such as Bloomberg’s document search functions, monitor and capture speculation well ahead systematically. By projecting the trend of data, and spotting out possible factors which affect the trend, effective decision making can be achieved.

Way to the Future

With today's increasingly complex environment, it is important not just to have access to the documents, but also to have access to the capability to study the documents systematically. In other words, the key to drive effective and efficient decision making is to gain insights and get context from data, analyze them with the needs of AI, and then collaborate. In this seminar, Emily has shared her view on how data is studied and how our technology may assist in this process. Our technology comes in the form of data, analytics, and community, they never stand alone.  All these are integrated into investment workflows to enable real time identification of opportunities. And from all these consistent and quality data, we frame out enterprise solutions. 

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Introduction -  How Web3, FinTech And Tokens Are Re-Inventing The World

The future of money is rapidly evolving as society shifts from a physical economy to an increasingly digital one.
From the metaverse’s increasing popularity to the democratization of money, innovation is clearly disrupting the traditional notion of money. This disruption is changing the way we commonly ‘make’, ‘spend’ and ‘manage’ money.
In this seminar, the speaker, Peter shared the ways of how large organisations applied for the technology evolution, followed by a fireside chat with Prof. Chun Kwong Chan, to discuss some of the interesting questions raised out from the participants. 

Key takeaway from the seminar

Peter started with a history about how currencies and technologies came along over the years, of which the changes had accelerated significantly especially in the past two years, from new tokens to new currencies and ventures into the growth of FinTech for HSBC, supported with the examples of HSBC sponsoring multiple digital brands experience. With HSBC adopting new technologies and driving innovation, this enabled us to sample and learn our ways of living and banking with new technologies, continuing to maintain the trust as a customers, as a business or as a government service provider.

How we are Digital Today

Digitalization has been progressively penetrating and changing the banking landscape. From facilitating the establishment of a new bank account, HSBC has launched a mobile banking experience which enabled customers to open a new account in less than five minutes, to the launch of PayMe digital wallet technology that allow people to have the easy invest technology through HSBC Reward+.  With effective multi-currencies (e.g. 28 different currencies) in PayMe wallet, this enabled customers to move money and transact without boundary and limitation of platforms and formats of payments. This enables HSBC businesses to grow with an advantage of competitiveness world globally today.

Significant Business: Mobile Banking

Currently HSBC deals with over 1.2 billion logins on the personal banking mobile app, of which 53% of them engaged with HSBC via the mobile app on a monthly basis, and over 95% of the retail banking transactions are done electronically. Especially, in recent two years, it has been accelerated by the need of people working from home while working remotely from home.

Moreover, Peter shared finance innovation in 22 areas, of which he called it “22 for 22” (some of the inspiring items are listed below)

• Currency— All currency is money, but not all money is currency.
• Growth— “Learn how to grow. In every way you can. Anything that doesn’t grow, dies.”
• Storability—There is a saying that if you don’t own your keys, you don’t own anything. There is also a demand from real customers who want a safe place to put their money and protection from things that can go wrong. Fraud has exploded in the last few years thanks to digitization.
• Blockchain—A database that is managed autonomously using a peer to per network, and a timestamping server to run a Ledger, and recording transactions.
• Tokens— “Every crypto is a token, but not every token is a crypto”. Tokens have been serving as a visible or tangible representation of a fact, quality, feeling for many years.

Q & A highlights:

This is about Proof of Stake in the Ethereum Network. Won’t this validation process centralize even further ETH?

Peter answered that they're trying to drive with proof of stake on a theorem is not centralization versus decentralization though it is the other big complaint. Nobody likes to do transactions where the cost of the transaction outweighs the cost of the asset. These protocols start to evolve and develop, as they deal with the different limitations that they have based on their decentralized or centralized nature, they're always trying to evolve and create platforms for amazing new use cases. He added that it's going beyond just the speculative asset of putting all people’s money into coin that can be used for trading.

What do you think if some people say that cryptocurrency promote illegal activities for example, selling drugs and, money laundering?

Peter believed that the fact that technologies could have both benevolent and malevolent use. It's safer to do a transaction in fiat currency than it is to do with a current transaction with Bitcoin as people can decode and identify using forensic means, such as the IP addresses and the wallets of a blockchain network is much easier to trace than the use of fiat currency.

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