2024 - The AI revolution in FS has begun

I was one of the founders of Accenture's Applied Intel practice and led AI for Financial Services for years. I also ran FS Digital at Accenture and Customer Sales & Service (CRM, call centres, front-offices, etc.) at Accenture Song. My practice totalled $7bn or 10% of Accenture's revenue. I have a PhD in Information Theory and was privileged to get classes from Marvin Minsky, founder of MIT's AI lab as my alma mater in Brussels was the first to have an AI lab in Europe, a satellite of MIT.

- Dr. Edwin Van der Ouderaa

My work at Accenture allowed me to pioneer Analytics, Big Data, ML and AI, especially in FS, from the front to the back office. My number crunching of the historic data of the top 3000 banks of the world, and the models on their balance sheets that followed from that, got me to be invited to be part of the project team at the Basel Committee, that created Basel III. In fact, I was asked to chair the Liquidity kick-off sessions.

Over the last ten years we pioneered a lot of deep Machine Learning applications in Banking and Insurance. They range from customer-related topics like propensity, pricing, KYC to underwriting and augmented credit scoring over AML and fraud to capital, liquidity and risk models. On the customer side we also worked on real-time personalisation and chatbots.

These models proved to be very powerful and rapidly became a mainstay across most banks. The wave of Digital transformations of the last ten years in Financial Services is in essence the result of this technology in combination with the advent of smartphones with 4G.

Powerful as 'classic ML' is, we were also acutely aware of its limitations. That is precisely why I believe that Gen AI can take things to a whole new level. Large language models allow us to do a whole set of things we could not do before and that represent a step change.

It starts with us being able to communicate with the system in normal language. This allows a richer instruction set for systems that often need to cater for very nuanced and complicated situations. One of the Achilles heels of existing decision systems is that it is very hard to code how the system needs to react to all the possible variations in input.