BaFin

Topic Fintechs CFTC’s FinTech Forward 2018 Conference

Date: 04.10.2018

Keynote by Felix Hufeld, President of the Federal Financial Supervision Authority (BaFin) at the CFTC’s FinTech Forward 2018 Conference on 4 October 2018

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Ladies and Gentlemen,

Thank you very much for the invitation to speak at the CFTC Fintech Conference today. Your invitation has reached me on rather short notice. But in life it's often the case that the spontaneous opportunities are the best in the end. Now I am here in the United States and I cannot expect that everyone knows what exactly BaFin is. BaFin was created in 2002 by the merger of the three German supervisory agencies, the Federal Banking Supervisory Office, the Federal Securities Supervisory Office and the Federal Insurance Supervisory Office. The aim of this merger was to create one integrated financial regulator that covered all financial markets. BaFin is now one of the biggest supervisory bodies in Europe and one of the last remaining integrated ones. This integrated and hopefully holistic view of risks can be an important advantage, especially in the case of innovative and overarching topics such as digitalisation.

It´s almost exactly ten years ago that the global financial crisis reached its peak. If we are now looking for new risks on the horizon, this quickly leads us to a trend with potentially far reaching impact: digitalisation.

Of course, digitalisation offers a lot of opportunities. But it is needless to say that as cautious regulators and supervisiors we definitely have to be aware of the risks as well, and therefore have to pervade this phenomenon in depth.

When you are reading any business magazine you will inevitably come across the statement that the financial world is currently undergoing profound technological changes. Digital networking is increasingly prevalent and new technologies are helping tackle ever more complex tasks. This trend is driven in particular by the availability of large quantities of data – big data (BD) – and by the improved opportunities for using this data – artificial intelligence (AI).

If we look at how BDAI works and the impact it has from a bird's eye view, it is quickly clear why BDAI applications can function as an efficient change driver for the financial market. Financial services heavily depend on information and evaluations thereof. With BDAI, it is possible to obtain a growing amount of increasingly precise information. If this information surpasses conventional processes, the providers making use of these evaluations will have a competitive advantage.

For instance, if a company is able to better assess the creditworthiness of an individual than its competitors, it can demand a more risk-adequate price and gain an edge over its competitors in the long term. Using BDAI could thus result in key competitive advantages on the financial market. Supervised firms will also take advantage of this, especially to increase their effectiveness and efficiency. Financial supervisors are therefore faced with the question of whether and how supervision and its foundations – regulation – need to be adjusted, and they will also have to examine which established principles should continue to apply. I would therefore like to offer four observations.

First observation:

Neither humans nor algorithms should be able to do whatever they want without oversight by people being personally responsible.

Decision-making and evaluation processes can be complex. If BDAI is to be used, it is important to ensure that the reasons behind decisions can still be traced.

If new types of algorithms or highly complex ones are used, companies often quickly refer to black boxes as an argument “I didn`t do it, the machine did it”: for instance, an innovative algorithm is generating highly precise forecasts, but the reasons why and the basis on which it operates cannot be traced and, unfortunately, cannot be verified by supervisors. This line of argument is unacceptable for supervisors, and management boards, too, would be well-advised not to accept this within their organisations as this potentially points towards a dysfunctional business organisation.

Experts in academia and (applied) research have also confirmed how important the explainability and transparency of algorithms is when they are used and have developed processes and tools for this purpose. It is possible to ensure the explainability of complex analytical processes as well. Ultimately, this is a prerequisite to secure the very principle of responsibility.

My second point focuses on automated processes:

Algorithmic decision-making processes should not be used without having appropriate backstops.

In live operations, algorithms often make use of many different data flows which may have been generated by algorithms themselves. This can result in self-reinforcing decision-making cascades. It could be worth taking a look at the tools available on the capital market: technological safeguards such as automatic circuit brakers are common practice there. Such automatic interruptions could also be useful safeguards for algorithmic decision-making processes – provided that they are also properly calibrated otherwise the number of mistakes and problems could increase even more.

Let us now look at point number three:

Market structures are changing and new systemic risks are on the rise.

Promising processes – such as deep learning – require huge amounts of data - keeping with motto: the more, the better - in order to generate interesting results that can form the basis for product and process innovations.

The advantages that BDAI processes bring will continue to grow if companies collect not only information on customer preferences but also information on their spending behaviour – for example, information relating to their current accounts or other payment accounts. Their BDAI algorithms could then be fed with far more accurate data. This shows that those who have the right to use abundant amounts of data, preferably also financial data, have huge advantages when developing new, promising BDAI-based products and services – especially outside the financial sector. And the use of these products, in turn, helps generate new data.

As these new technical opportunities will certainly attract – and already do – dominant platform providers to enter the financial market, regulators and supervisors will certainly be confronted with a variety of fundamentally new challenges. They do have the ability to reshape customer behavior and market structures all over the world and could very quickly become systemically important. However, such providers could also become systemically important indirectly, for instance if they sell information on how to calculate risks more precisely or provide BDAI infrastructures to a large number of players on the financial market.

If the associated risks are no longer within the organisational structure of supervised firms, there is a danger that they can no longer be fully identified and subjected to proper oversight. An analogy to the role of rating agencies comes to mind. It is therefore necessary to examine whether the definition of systemic importance in the supervisory sense and, thus, the possibility of introducing mitigating measures should be revised in order to take into account new business models and market structures.

Supervisors like BaFin, which also have a mandate to protect consumers, need to carefully consider some other aspects as well. This is why my fourth point is devoted to consumer protection and consumer sovereignty.

Data innovations still require consumer trust – maybe more than ever.

In a world full of platform providers, consumers need to be aware of the fact that from the moment they grant these companies access to their data, they are giving them the key to their private lives. In doing so, they are opening their virtual front door and allowing companies to draw conclusions about their health and credit default risks, to name but a few.

And when companies understand how they can match financial transaction data and behavioural data with information on the needs and preferences of their customers, they can then draw highly precise conclusions on every customer’s willingness and ability to pay under very particular circumstances.

In some cases, this may not be a bad thing and may even accommodate the wishes or needs of consumers in that they are offered tailored products. But what happens if such personal data is used for other purposes as well or is even misused? There is a thin line between acceptable or even welcome price differenciation and not acceptable price discrimination. BDAI applications will make this distinction even more difficult. I believe that, if things go wrong, there is a risk that customers will lose trust in the companies they have entrusted their sensitive financial data to.

Data leaks can also have a lasting adverse effect on consumer trust. BDAI technology will efficiently show its full potential only if companies succeed in building and maintaining trust by using their customers’ data properly and in accordance with the law.

And this is why companies would be well-advised, also out of self-interest, to carefully consider the extent to which the value added by monetising personal data outweighs any potential negative effects on both the company’s reputation and consumer trust, even if customers have formally given their consent.

Ladies and gentlemen,

I hope my little tour d’horizon through various aspects of digitalisation and the penetration of the financial markets with BDAI technology has given you an idea of the size of the challenges that financial regulators and supervisors will have to deal with in the years to come. Some of the content I talked about can also be found in an article that was recently published in our new publication “BaFinPerspectives”1 and in BaFin’s 200 pages report “Big data meets artificial intelligence”2. Both are available in English and can be downloaded from BaFin`s webpage at your discretion.

Another topic that has the potential to profoundly change financial markets in the future is distributed ledger technology, with all its possible applications. But since you explicitly asked for a short keynote, I will better finish at this moment and leave these topics to my fellow speakers. A brief look at the agenda has shown me that you have not forgotten topics like crypto asset markets or tokenization

Thank you for your attention. I am looking forward to your questions.

Footnotes:

  1. 1 Felix Hufeld: “Supervision and Regulation in the Age of Big Data and Artificial Intelligence”, BaFinPerspectives, Issue 1/2018.
  2. 2 “Big data meets artificial intelligence - Challenges and implications for the supervision and regulation of financial services”, BaFin, published in August 2018.

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