In business-critical fields, Artificial Intelligence can if applied naively or carelessly do harm, Professor of Finance Norman Schuerhoff says in an interview with finews.com. The Revolut scandal is a good example.


Professor Schuerhoff, what is your contribution to the «Master Class»?

In my most recent SFI Master Class, I teach Machine Learning and Artificial Intelligence (ML/AI) and their applications in banking and finance to banking executives.

Why did you choose this topic?

It is a revolution. ML/AI are widely considered to be the new  «internet» for advanced analytics, automation, pricing and many other aspects of business processes and daily life. The consultancy Gartner estimates that ML/AI adoption by businesses across economic sectors will reach more than 75 percent by 2022. Banking and finance are no exception, though, they are laggards in the «race».

Norman Schuerhoff's Master Class: «Machine Learning and AI: Applications in Banking and Finance», May 6, 2020

At the same time, McKinsey estimates that more than 50 percent of big data projects fail in one form or another. Businesses either have no analytics strategy, analytics capabilities are isolated from business, or there exists a skills gap that requires «analytics translators.» It is therefore important to bridge the gap between business and analytics.

A professor of finance, though it is not my core competency, can easily fill this void. In my own research, I both study business needs and behavior and I apply advanced data analytics. The combination of the two fields comes naturally to me. I place myself at the interface between analytics and business advisory, more than maybe a typical line manager in a bank. It is therefore natural for me to teach this class.

Who is your audience?

My target audience are banking executives who either have been exposed already to ML/AI and want to learn more deeply about the techniques and applications or who simply want to learn what is the buzz is all about. My course serves both audiences.

My experienced co-instructor Annika Schroeder from UBS and I start with the basics of ML/AI and build up the knowledge to allow participants to leave the room with a critical mind.

«Clearly, Machine Learning works best in data and information-driven enterprises like banks.»

We confront the participants very quickly with real-life business scenarios where ML/AI can be applied and can be helpful. We illustrate why and where a business understanding of ML/AI is crucially needed.

What insights may attendants expect?

ML/AI is evolving as we speak. There is no one right answer to how and where to best apply ML/AI. But, clearly, ML/AI works best in data/information-driven enterprises like banks.

At the same time, ML/AI technology has reached a critical mass. Attendants will be encouraged to build up skills in their own organization and learn how to go about combining smart humans with smart machines to make their organizations future-proof.

Where is – in our daily life – ML/AI already operational?

My online shopping sites have started to tell me what its algorithms believe I like, need, and want to buy. Their algorithms also raise the price just before I am ready to buy – I wonder why. An app on my phone tells me which road to take to get from A to B and how long it will take me. Soon, my fridge will refill itself.

What are the fields of practice in the financial industry so far?

ML/AI can and already gets applied in many areas. ML/AI can be used to do the same cheaper, do more of the same, do the same better, and do new things. There are five types of use cases in banking and finance.

  • Intelligent automation helps to improve mid- and back-office efficiency. Applications include early warning systems, compliance, regulatory reporting, and trade processing.
  • The second pillar I call business intelligence and cognitive enhancements. This includes ML/AI applications to better understand client needs and improve client interactions.
  • The third pillar is knowledge discovery. It includes investment research, corporate statements, and legal discovery.
  • New growth are ML/AI applications for idea generation, smart alpha, and risk containment. Quantamental investment and risk management, trading recommendations and client advisory fall under this pillar.
  • Finally, new ML/AI enabled and based business models are still the sound of the future.

What are the ML/AI game changers in today's financial industry?

There exist external and internal game changers. The external ones are regulation and margin pressure in a quickly evolving post-financial crisis environment. In the current low interest rate environment, banks face tremendous margin pressure because their classical business models are not as profitable as before. New entrants, especially from the ML/AI domain, put additional competitive pressures.

«Technology in banking is still specialized, legacy, siloed, and regulated»

In the medium-term, the financial sector will better understand and explore the value from the drove of data it is sitting on. Banks are essentially data companies with banking licenses. Banks need to better internalize the idea that their value chain is built on data. ML/AI can then become the enabler of an ecosystem that serves client needs with simple, secure and enriching interaction.

Banks are not yet, or at least not enough technology companies. Technology in banking is still specialized, legacy, siloed, and regulated. The bank of the future will look different from today’s.

Are there cultural differences between American and Swiss banks when applying ML/AI?

U.S. banks are clearly much further advanced in some areas of ML/AI applications than Swiss banks, especially when it comes to adopting ML/AI in core competencies. Some U.S. banks have thrown hundreds of million dollars at developing ML/AI applications in various areas.

«In business-critical applications, ML/AI can if applied naively or carelessly do harm»

Yet, Swiss banks have good reasons to be cautious. Especially in business-critical applications, ML/AI can if applied naively or carelessly do harm. The Revolut scandal of 2018 is a good example.

Where do you see further room for ML/AI developments?

Deep learning and neural networks work well in Google-style applications such as face recognition. It is less clear that these techniques create long-term value in a financial market context. Financial markets are amazingly complex with millions of participants with different preferences, know-how, and sophistication. The stochastic nature of the interaction in financial markets defies the laws of physics.

In the long-run, the financial sector may develop its own tools and techniques specially geared towards understanding financial markets. Finance needs its own methods. But we are not there yet.

What are the most recent findings in your research?

In one of my recent research projects, I show how ML/AI is revolutionizing the trading of financial securities. Algorithmic and high-frequency trading have long been an integral part of liquid markets like stock exchanges.

«In one of my research projects, we explore if a machine can replace a Chief Financial Officer»

However, the trading of more illiquid and opaque financial securities, such as corporate bonds, has until recently been very different and insulated from technological innovations. With the advent of electronic trading in over-the-counter (OTC) markets, ML/AI become crucial technologies for pricing and trading.

In another of my research projects, we explore if a machine can replace a Chief Financial Officer (CFO). The role of a CFO is to advise the CEO on all financial matters and to choose the optimal capital structure and financing policies. Like a computer can learn from human drivers how to best navigate a car, a computer may learn from CFOs’ behavior how to best run the financing of a firm.


Norman Schuerhoff is a Professor of Finance at the University of Lausanne. His work has been published in the top academic journals in finance and he has won several prestigious publication awards. He is a six-time winner of the CFA Institute Research Challenge in Switzerland and was World Champion for 2018. His main research interests lie in financial intermediation, corporation finance, corporate governance, market microstructure, and asset pricing.

In ongoing research, Professor Schuerhoff and coauthors study the municipal bond market—the largest and most important capital market for state and municipal finance in the U.S. With «green» bonds issued at a premium due to strong investor demands, the municipal bond market has led the development of responsible investing.