Financial technology – an introduction

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By Ingrid Goodspeed, Governor of the South African Institute of Financial Markets

1. Introduction

Financial technology (fintech) is not new. From automated teller machines (ATMs) in the late 1960s and 1970s to the rapid expansion of internet-based and mobile banking during the beginning of the 21st century financial institutions, particularly banks, have used technology to deliver products and services to customers for decades. However what is new is the rapid pace of innovation and the entry into financial services of both new technologies and non-financial players with substantial potential benefits to consumers in the form of lower costs, more competition, efficiency gains, increased access to financial products and markets.

While these technology-enabled innovations have the potential to address certain longstanding and widespread challenges of the financial system in the areas of inclusion, competition, literacy and integrity, they bring a new set of both conduct and prudential risks that should be reflected on by market practitioners and more specifically regulators. Ideally regulators must provide for new classes of entrants to the financial services industry while safeguarding financial stability, maintaining level playing fields, protecting consumers and their privacy, ensuring market integrity and guarding against money laundering and cybercrimes.

2. What is financial technology?

Financial technology is an umbrella term that incorporates a wide range of new business models and technical innovations that have the potential to transform the financial sector[1]. Breakthroughs in technological capabilities – hardware, software, telecommunications, data analytics and artificial intelligence – have provided new ways to communicate, store and process information and enabled the development of a number of new financial products and services including crowdfunding, peer-to-peer lending, robo-advisors, high-frequency trading, smart contracts and distributed autonomous organisations.

3. Fintech in action

There appears to be a virtual torrent of fintech innovations coming to market. The matrix below attempts to highlight the most important and potentially transformative of these in respect of financial sector activities impacted (rows) as opposed to key technology innovation categories (columns).

Not specifically included are fintech enablers that underpin any if not all aspects of fintech innovation. These include internet of things, big data (including social media), internet, natural language processing, Application Programming Interfaces (APIs) and smart or advanced analytics (generally statistical). For example innovation in credit scoring for potential borrowers with no or limited credit history could not be achieved without big data, statistical analysis and the internet of things while high-frequency trading requires sophisticated statistical analytics combined with terabytes of data and instantaneous access to numerous market data feeds.

Fintech in action

Tech Category Artificial intelligence (including robotics) Distributed ledger technology Infrastructure and distribution software platforms
Financial services
Banking including corporate finance Information discovery for M&A, private equity Smart contracts Letters of credit Transaction banking Cashflow management Financial information aggregators, comparators and monitors
Regulatory and compliance management (Regtech) Money laundering and terrorist financing Stress testing Know your customer information Delivery of regulatory requirements
Financial advice and personal finance Robo-advisors Financial modeling tools to improve credit health and make major financial decisions Financial literacy Financial wellness guidance
Institutional investment management Market sentiment analysis to determine breaking, market-moving events for investors Transfer of asset ownership (DVP basis) Bond trading Buy-side investment research repository Investment management platform
Insurance (insurtech) Predict lapses and claims fraud Detect and flag risks in the home before they become claims Smart contracts Insurance broker platforms Claims resolution platforms P2P Insurance
Payments and remittances Wholesale payments, clearing and settlement Real-time (instant or immediate) payments between individuals and businesses Cross-border remittances using crypto-currency
Retail investment Robo-advisors Distributed autonomous organisations P2P lending Social trading and investing Self-service investment and wealth management Social network for investors and traders Crowd-sourced equity market research
Retail lending including mortgage lending Credit assessment and scoring Transfer of property ownership (DVP basis) P2P lending Automate mortgage application process
Securities services including trading High-frequency trading Market sentiment analysis to determine breaking, market-moving events for traders Trading of securities Post-trade activities such as settlement, corporate actions and record-keeping Stock exchanges
SME lending Credit scoring of small business Equity crowdfunding
P2P lending Capital provision:
Angel and start-up funding and investing

The technology categories will be discussed in more detail:

Artificial intelligence

Artificial intelligence is basically intelligence displayed by computers evidenced by their ability to solve problems or reach goals with limited resources. AI ranges from expert systems that simply emulate the decision-making ability of a human expert by following instructions provided by a programmer to deep learning systems that attempt to teach computers how to talk, listen, read, reason and think like humans and can be so complicated that even the engineers or programmers who designed them may struggle to isolate how or why the systems make their decisions. The “fuel” for AI, particularly deep learning, is vast quantities of data.

The table above shows that AI has many applications. Possibly the most developed of these are high-frequency trading, robo-advice and credit scoring. High-frequency trading attempts to generate mainly arbitrage profits algorithmically by doing many small-size and profit trades with short holding periods of frequently less than one second. Robo-advice systems draw on algorithms, statistical analytics and big data to assist investors develop their individual investment portfolios based on their risk tolerances, investment goals, investment horizons, and personal preferences. Recent developments in credit scoring algorithms allow lenders to take advantage of machine learning and a myriad of data points to more accurately assess potential borrowers (including small businesses) that traditional credit scoring systems overlook because borrowers have little or no credit information.

Other AI applications include:

  • Information discovery for merger and acquisition and private equity deals
  • Interpretation of transactions data to recognize money laundering and terrorism financing
  • Modeling, scenario analysis and forecasting for stress testing and risk management
  • Assessing market sentiment to determine breaking and potentially market-moving events for institutional investors and traders
  • Predicting insurance lapses and insurance claim fraud
  • Detecting and flagging insurance risks in homes before they become claims.

Artificial intelligence: the end of mankind?

Some thinkers, philosophers and futurists warn of the serious dangers posed by AI as computers improve in competence at an exponential rate eventually reaching the point at which they outstrip human capabilities in virtually every cognitive domain. The Warner Brothers science-fiction crime drama television series Person of Interest is an entertaining exposition of this issue.

Elon Musk believes AI is humankind’s “biggest existential threat” and suggests that extreme care, if not regulatory oversight at national and international level, is required for the AI ecosystem1.

The author of Superintelligence, Nick Bostrom believes AI is potentially more dangerous than nuclear weapons2.

Stephen Hawking thinks that the development of full AI could be the end of the human race. “Once humans develop artificial intelligence that would take off on its own and redesign itself at an ever-increasing rate, humans who are limited by slow biological evolution couldn’t compete and would be superseded”3.

Bill Gates agrees with Elon Musk and others on this and does not understand why some people are not concerned1.

Then again Albert Einstein a German-born US physicist who died in 1955 is attributed with saying “computers are incredibly fast, accurate, and stupid. Human beings are incredibly slow, inaccurate, and brilliant. Together they are powerful beyond imagination”.

  2. Bostrom, Nick. 2014. Superintelligence. Oxford University Press Oxford, UK

Distributed ledger technology

Distributed ledger technology, also known as blockchain, is essentially a database (the ledger) maintained collaboratively by a number of participants whose computers use a consensus mechanism to update the database on a regular basis. Once changes are agreed they are made unchangeable with complex cryptography. Information preserved in this way can be used as proof of ownership.

Distributed ledger technology underpins software-automated transactions or smart contracts, which are contracts with program protocols that automatically execute when pre-defined conditions are met such as call provisions in bonds and claim triggers in insurance policies. Distributed ledger technology with smart contracts can provide a common ledger for letters of credit that allows all counterparties to have the same validated record of transaction and fulfillment.

Securities market applications of smart contracts and distributed ledger technology include trading of securities, settlement and clearing, corporate actions, and management of margin positions and collateral.

Further applications of distributed ledger technology are:

  • maintaining know-your-customer (KYC) information. Keeping digital identities on a national distributed ledger could enable timely, cost efficient and reliable KYC checks or verification for individuals or organisations
  • providing asset registration facilities. For example the registration of real estate ownership and encumbrances such as mortgage bonds and liens.

A distributed autonomous organisation or decentralized anonymous organisation (DAO) is a collection of individuals whose relationships are governed by smart contracts. One use of a DOA is as a collective investment scheme that enables investors to pool capital and then collectively identify and decide to invest in projects or assets. In July 2015 the world’s first DAO venture capital fund was launched on the Ethereum open-source, public blockchain platform using its virtual currency called Ether. Ether can be exchanged for voting and ownership rights in the fund. All decisions are decentralized and taken by investors through digital voting.

Infrastructure and distribution software platforms

A software platform[2] is an all-encompassing term that describes innovative technology that could or is replacing current legacy financial services activities and includes different applications of existing products and services and the unbundling of financial services traditionally offered by service providers such as banks, brokers or investment managers.

The most well-known applications are crowd funding, peer-to-peer (P2P) lending, financial information aggregators:

  • Crowdfunding is a way for small businesses to raise finance from a large number of individuals rather than from specialised investors such as banks, business angels, venture capitalists. Each individual provides a small amount of the funding requested. The type of contributions – from donations to loans and equity – and related rewards may vary depending on the platform, the type of firm and the project.
  • Peer-to-peer lending platforms allow investors, alone or with others, to provide financing to borrowers. Lenders / investors range from individuals to institutions and can receive monthly interest income in addition to capital repayments.
  • Aggregators are non-banks that collect detailed consumer financial account data information with the permission of the consumer. Aggregators retrieve and store the data, make it available to the consumer, and use it to make targeted offers for financial products and services including cash, debt and financial management tools such as automatic savings programs, budgeting tools, investment analysis and online bill payments.

Regtech consists of technology platforms that help the financial services industry to better manage regulatory and compliance requirements. Regtech initiatives include identifying clients and companies as required by anti-money-laundering regulations and monitoring a financial institution’s internal culture and behavior to comply with customer protection and market conduct requirements.

4. Unintended consequences of fintech

While fintech has the potential to address some of the longstanding and widespread challenges of the financial system, regulators should be aware of the potential risks and unintended consequences including flash crashes, mismanaged initial public offerings (IPOs), cybersecurity breaches and catastrophic algorithmic trading errors.

Specific examples are:

  • The week of 6 August 2007 several hedge funds suffered substantial losses as a result of the automatic liquidation of one or more similar but independent equity market-neutral portfolios. The nickname given to the event was quant meltdown.
  • On 6 May 2010 the Dow Jones Industrial Average suffered its largest one-day point decline of 600 points in the space of five minutes. The event is known as the flash crash.
  • On 18 May 2012 Facebook, launched its highly-anticipated IPO on NASDAQ. Due to a technical bug the launch was botched. NASDAQ paid its customers USD62million for losses accrued because of its mishandling of the IPO.
  • On 1 August 2012 Knight Capital Group issued a deluge of unintended orders electronically. Unable to void these trades Knight Capital was forced to liquidate them at market prices resulting in a USD457.6 million loss to the company.
  • On 15 October 2014 the so-called Treasury flash crash occurred. Yields in benchmark 10-year Treasury bills traded in a range of 35 basis points between market open and close, a highly-unlikely seven standard deviation intraday event without apparent cause.
  • In June 2016 a Ethereum-platform DOA was hacked due to a smart contract weakness and about USD60million of its digital money Ether was stolen.

5. Regulatory response to fintech

Financial sector regulators are often accused of being too lax and slow to respond properly to financial innovation: cases in point are sub-prime securities, credit default swaps, collaterised debt obligations and other pre-2008 innovations as well as products misrepresented to end-users such as interest-rate swaps for small businesses and complex retail investment products. The counter argument is that many financial innovations have improved the economic welfare of market participants. Examples are mobile banking, payment innovations, index funds and a variety of pension savings vehicles.

Most financial sector regulators are still developing their policy approach to technological innovation in financial services. For example in March 2017 the US’s Office of the Comptroller of the Currency issued a white paper to launch formal discussions between regulators and fintech industry leaders. In the same month the European Commission published a public consultation on technology and its impact on the European financial services sector. The UK’s Financial Conduct Authority has published a number of discussion documents on fintech. In the 2017 budget review, National Treasury advised that a fintech regulatory framework would be part of the Conduct of Financial Institutions Bill to be introduced in 2017. The framework could include a regulatory sandbox to encourage innovation within a risk-controlled environment.

Since newly introduced innovations generally have a narrow user base and limited scope for creating systemic harm, the majority of financial sectors regulators are allowing fintech to flourish and are even encouraging it. The UK seems to be leading in this regard: the Bank of England established a FinTech Accelerator to explore proofs of concept around fintech innovations. In addition the UK’s prudential and conduct authorities have changed their licensing processes to support new innovative business models. The Financial Conduct Authority has established a regulatory sandbox and signed co-operation frameworks with other jurisdictions including Japan, Singapore and Korea to support innovative fintech companies across jurisdictions. The Monetary Authority of Singapore has set up a regulatory sandbox to allow fintech experimentation so that promising innovations can be tested in the market and have an opportunity for wider acceptance. The Monetary Authority of Singapore has also signed an agreement with the Australian Securities and Investments Commission (ASIC) that intends to allow fintech firms to establish initial discussions in each other’s markets faster and receive advice on required licenses.

The challenge for regulators lies in recognising and limiting the growth of flawed fintech products and services before they become widely distributed. Ideally a regulatory response should consider

  • The implications for and impact of fintech innovations on the key financial policy objectives of financial stability; consumer and investor protection, market fairness and integrity, and financial inclusion including how developments could disrupt systemically important markets, impact the safety and soundness of existing regulated financial institutions and change the level of cyber and operational risks faced by regulated financial institutions and the financial system as a whole
  • Fintech innovations that constitute traditional financial services activities by another name and should be regulated as such to ensure that the same activity is subject to the same regulation irrespective of the way the service is delivered, so that innovation is enabled and level-playing field preserved
  • The benefits, opportunities, challenges and risks of new business models and financial technologies
  • Barriers to entry, innovation and adoption of fintech
  • Whether and what international standards would be beneficial.

6. Conclusion- a macroeconomic viewpoint

From a macroeconomic viewpoint (the dismal science) fintech advances may lead to higher incomes for people whose skills are complemented by the technology, but not for those whose skills are substituted by it. The result may be worsening income inequality, which can lead to weaker macroeconomic outcomes and social and financial instability. The polarizing effect of technology on income distribution could be amplified by a winner-takes-all effect, which comes from the market power that new technologies often give their inventors (see footnote 2 for examples). Several jurisdictions have put research programs in place to better understand these issues and investigate how fintech is unfolding and affecting the economy and to encourage fintech innovation and ensure inclusive prosperity while managing harmful side effects.

During the industrial revolution circa 1840 John Stuart Mill[3] wrote that “there cannot be a more legitimate object of the legislator’s care” than the interests of those whose livelihood and employment are diminished by technology. At a national policy level surely this remains as true in the current fintech age?


Awrey, Dan. March 2017. Artificial Intelligence versus human nature. University of Oxford: Faculty of law
Economist. May 2015. Artificial intelligence: rise of the machines.
Economist. April 2017. Click to trade: digitization shakes up corporate bond markets.
European Commission. March 2017. Fintech: a more competitive and innovative European financial sector.
EY. 2016. Capital markets: innovation and the fintech
Financial Conduct Authority. November 2015. Supporting the development and adoption of RegTech.
Goodspeed, Ingrid. May 2014. Bitcoin in the South African Financial Markets Journal,
Goodspeed, Ingrid. October 2012. High frequency trading in the South African Financial Markets Journal,
IOSCO. February 2017. Research Report on Financial Technologies (Fintech).
Knight, Will. April 2017. The dark secret at the heart of AI. MIT Technology Review.
Lo, Andrew W. May 2016. Moore’s Law vs. Murphy’s Law in the financial system: who’s winning?
OECD. 2015. New approaches to SME and entrepreneurship financing: broadening the range of instruments.

Websites accessed in April 2017

[1] The term fintech is sometimes used to refer to the firms that use innovative business models and technology to enable, enhance and better deliver financial services. This definition includes start-ups, new entrants, maturing companies and even non-financial-services companies such as telecommunication providers and e-retailers.

[2] The term “platform” is often used to refer to an online marketplace of the kind operated by technical giants such as Facebook, Google, Amazon, Alibaba, Tencent and Uber.

[3] An English philosopher, political economist and civil servant and one of the most influential thinkers of the nineteenth century

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