Exploring the transformative role of AI and data in financial services

Professor Douglas W. Arner, the Kerry Holdings Professor in Law at the University of Hong Kong, delivered a virtual keynote address at the “Global Tech Meetup: Sri Lanka Edition,” recently held at the Hilton Colombo.

His speech explored the transformative role of AI and data in financial services, emphasizing the growing intersection of finance, technology, and regulatory frameworks.

Following are the excepts of his speech: 

When we look at the bigger picture, we can say that the dominant theme over the past two decades in finance has been the role of technology. Certainly, if we look to identify amongst the biggest issues over the next five years from the standpoint of technology, one of those is most certainly data. 

Data can be viewed from the standpoint of strategies, risks, and artificial intelligence, and I think if we're thinking about data in finance, it's important to realize that data and finance have a long history. 

If we think about finance, finance is one of the world's most digitized industries. It's also one of the world's most globalized industries and one of the world's most regulated industries and as a result, we've seen for an extended period of regulatory approaches to data in the context of financial services from the standpoint of securities trading, fraud control, risk management, asset management, compliance, more recently reg tech and Supertech, efforts from the standpoint of credit risk and cybersecurity. 

Now we're seeing the evolution of a range of developments in the context of debt capital markets, foreign exchange, and insurance. 

But at the same time, we're running into an increasing intersection between these traditionally sectoral approaches to data in the context of finance and some of our more general approaches to data across major jurisdictions and we're seeing the rise of an increasing range of tensions and even conflicts about data, particularly between the US with its market-based approach, the EU with a very rights-based approach to data and China with a much more sort of state-based approach to data. 

And these issues around approaches to data are also increasingly intersecting with questions around both competitiveness as well as risks in the context of artificial intelligence and finance for a variety of reasons has been one of the leading sectors implementing or applying AI over an extended period of time and one can say that this largely relates to the fact that it's been a highly digitized industry with large volumes of standardized data, heavy incentives and the availability of resources and all of these have come together in ever more competitive approaches to the use of data in financial services but also to the role of artificial intelligence in financial services and of course if we think about the recent advent of DeepSeek coming in the context of generative AI from China, DeepSeek was of course developed by a quantitative finance trading firm so one can see these sorts of intersections directly and I think when we are thinking about data and AI risks it's important to look at these from a number of different directions. 

First, to realize that AI brings with it potential macro issues, macro issues that could range from the standpoint of potential risks of sentient AI to issues around potential mass unemployment or economic instability or energy consumption, and these macro risks, macro issues require very different approaches from some of our other areas and these in particular one can think of in the context of sectoral issues such as fairness, behavioral sorts of issues in the context of finance, health, and others. 

One can see a variety of infrastructure issues but where the biggest picture or the biggest take up has really been being in the context of malicious actors, criminals, hackers, and terrorists have become increasingly professionalized but also they're not concerned about ethics, risks or some of these concerns that we may have in the context of other areas. 

Now from the standpoint of thinking forward in addition to these issues around malicious use and cultural aspects, we're also seeing aspects around how people are using AI, and certainly what we've very much seen is that there are real benefits from the standpoint of expert professionals. But the real challenge is from the standpoint of those who don't have that sort of level of expertise. As we go forward I think the key message around this intersection between data, finance, and artificial intelligence is that countries are spending ever more effort from the standpoint of thinking about how can they maximize the benefits of aggregate data. Including for uses such as finance or to support the development of artificial intelligence while at the same time how can they go about addressing this wide spectrum of risks. In many ways where all of this is coming together is narrowly in the context of discussions around credit data access and widening those but more broadly from the standpoint of thinking about how we go about developing systems to maximize the benefits of things like open data, open finance, open banking through enabling infrastructures.