The banking industry is becoming a digital rather than a physical system – UJ’s Prof Tshilidzi Marwala

The Vice-Chancellor and Principal of the University of Johannesburg (UJ) and the co-author of the book Artificial Intelligence and Economic Theory: Skynet in the Market, Prof Tshilidzi Marwala recently penned an opinion piece, The Banker of the Fourth Industrial Revolution, published by The Daily Maverick, 13 September 2018.

The Banker of the Fourth Industrial Revolution

The banking industry is becoming a digital rather than a physical system. I wonder what sort of leaders should be running a modern bank. Should they be accountants or engineers, or both?

I recently visited the boards of Barclays Africa and the Discovery Group to talk about the Fourth Industrial Revolution and its impact on the banking industry. I marvel at the pace at which the banking industry is migrating from the physical branch to a computer application.

I have not visited a branch of my bank this year because I can perform everything I used to do at my physical branch on my phone app. I also realise that this phone banking application is not perfect, as it is only available at 80 per cent of the time due to technological challenges.

The banking industry is becoming a digital rather than a physical system. I wonder what sort of leaders should be running a modern bank. Should they be accountants or engineers, or both? I recall that when I was completing a PhD in artificial intelligence at the University of Cambridge 20 years ago, many of my engineering classmates went to work for banks, with some running large Wall Street financial companies.

The Fourth Industrial Revolution is shrinking the world of work at a rapid rate. Machines that are powered by artificial intelligence (AI) now conduct tasks once conducted by humans. AI is a computer technique that makes machines think like humans. There are three main types of AI — machine learning, computational intelligence and soft computing.

Machine learning is a statistical approach to AI and it has a subgroup called deep learning. An example of machine learning is a neural network, which is inspired by the neurons in our brains. Deep learning is when these neural networks are large, have multiple layers, and are thus able to perform complicated tasks such as identifying people’s faces and reading fingerprints.

Computational intelligence is a type of AI that derive from the collective intelligence of a group of biological organisms. An example of this is the colony of ants, which uses collective intelligence to build complex anthills. Their intelligence was uncovered by Afrikaans poet Eugene Marais in 1928 and was published in his classic book Die Siel van die Mier. Computational intelligence is used to schedule difficult problems such as using computers to manage queues in hospitals and finding the shortest distance between two points in the Google Maps application.

Soft computing is the approach to AI that handles the shortage of data. An example of this is fuzzy logic, which can be used to extract expertise from a person to a machine. Big data analytics use AI to handle large amounts of data.

The banking system works by accepting deposits from customers, borrowing money from the Reserve Bank and raising money from the capital markets by issuing bonds and then creating credit to lend to whoever applies and qualifies for loans. For the bank to be profitable, the effective lending rate should be greater than the effective borrowing rate. For the bank to be effective it must have competent people who understand credit and debt, interest rates, repo rates, systems and so on, so that they can create products that can be sold to customers.

The advent of AI is reducing the need for people in the banking system. Recently, Michael Jordaan and Yatin Narsai opened Bank Zero, which has no branches and operates only in the digital space. This necessarily means that computers perform many activities once done by people. Because people doing such tasks are intelligent, these computer apps should also be intelligent — and AI can only enable this.

For example, for a bank to decide to offer a credit to an individual, it needs to evaluate the creditworthiness of the individual. This is done so that the bank minimises the chances that the individual concerned will default.

Conventionally, human experts evaluate the income of the individual, his monthly expenses, where he lives and so on, and then give him a credit score. This credit score determines the interest rate at which this person receives this loan.

In my book Economic Modelling Using Artificial Intelligence I describe multi-agent AI systems which have been observed to perform credit scoring better than humans. This is because they are able to go to the internet, search for information on the prospective customer, and incorporate this information into the credit score.

Banks have to make their decisions with incomplete/imperfect information and with customers, they have limited knowledge about. This phenomenon is called information asymmetry and it won Joseph Stiglitz, George Akerloff and Michael Spence a Nobel Prize in Economics. AI breaks down the barriers of information asymmetry and promotes fair trade in the banking sector.

Behavioural economics has taught us that humans can never be fully rational and that they make their decisions based on truncated logic and information. This idea of the limited rationality of humans won Daniel Kahneman and Herbert Simon Economic Nobel Prizes. The influence of human behaviour in the market is to curtail the efficiency of the markets. The replacement of human decision-makers with AI decision-makers makes markets more efficient and thereby promotes fair trade.

If markets are not efficient, this means people can gain from the market without putting in the effort. This can result in inefficient allocation of resources, thereby curtailing productivity.

Banks sell financial instruments named derivatives and one example of these is options. An option is a contract that gives an entity a right and not an obligation to buy or sell a particular good, at a particular time and at a particular price, called the strike price. Pricing these options has been a difficult task and Marion Scholes and Robert Merton were awarded the Nobel Prize for pricing such options.

The advent of AI has made it possible to price these instruments better. The advent of fast computers makes it possible to simulate the behaviour of these instruments. Recently at the University of Johannesburg, we developed a mechanism of pricing options with imprecisely defined parameters using the type of AI called fuzzy logic.

It is evident that the bankers of the future should understand both the finance and technology and therefore must be both engineers and economists. As universities, how do we train such people?

One way of doing this is to use an example of a young man, Msizi Khoza, who is a banker, but studied an undergraduate degree in electrical engineering and a postgraduate degree in economics.

Of course, this required him to have two degrees instead of one. The other is to introduce an undergraduate degree in financial engineering where concepts from science and technology are merged with concepts from financial and economic sciences.

If we fail to exploit these emerging trends, we will have a banking system that will struggle to lead in the Fourth Industrial Revolution.

The views expressed in this article are that of the author/s and do not necessarily reflect that of the University of Johannesburg.

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