A perfect storm of pressures which has been building on the financial services industry for years has come to a head.

Covid-19 has been a catalyst for change for institutions who have been facing a build-up of competitive and regulatory pressures and now have to face the urgency of making their business models fit for purpose. AI, for many years a “nice to have”, has now become integral to running a financial services business efficiently and profitably.

US banks are ahead of the curve here: many have already gone through their AI transformation and as a result are in better shape for the next decade. Many European institutions need their own AI revolution in order not to become obsolete. Some, like ING, have already managed to do this, but many are behind the leading US banks in putting AI at the heart of their operations.  

There are three big strategic threats to the wholesale banking system in Europe, the “three horsemen” of the potential Apocalypse: regulation, market risk and competition. None of these is new, but COVID and other market shocks over the past year have brought the challenge into sharp focus.

Compliance is the first major front. Regulatory changes come into effect over the course of the next year which require forensic oversight of large amounts of documentation: a task that is too slow, error-prone and expensive to be completed manually. LIBOR, Basel IV and Dodd-Frank QFC recordkeeping requirements place more and more demands on financial services companies and many simply aren’t adequately prepared.

This has now reached a tipping point for the industry: managing the volume of compliance is already highly burdensome for many institutions and without efficient processes it will impact profitability. Eigen has worked with a number of the world’s largest institutions to help them create technology-driven solutions to managing compliance. For example, we’ve helped Goldman Sachs to handle new regulations and ongoing reporting requirements such as LIBOR transition and Dodd-Frank QFC.

The second area is market risk. The volatility of markets in the past year means that transparent oversight is critical. This is where AI comes into its own. AI technology can automate the processing and analysis of the documentation which underpins much of the financial system, from loan agreements to insurance policies. This means that work which would previously have entailed long hours can be accelerated, allowing for vastly improved efficiency and speed and, critically, much better oversight of the compliance requirements which regulators mandate.

AI gives institutions the ability to remain vigilant and to keep abreast of risks with much more efficiency than ever before. With market conditions likely to remain volatile throughout 2021, fast, responsive and data-backed decisions aren’t only essential for each institution, they are critical for the health of the financial system as a whole.

But if institutions are going to invest in technology it needs to demonstrate that it not simply a cost, and that it supports longer-term growth and, in turn, future profitability. This takes us to the third of the “horsemen”, the longest-term but potentially the greatest challenge: competition.

Competition takes several forms, such as challengers from the technology industry such as Alibaba and Amazon and fintech operators such as digital banks. While technology companies are not going to be able to replace international wholesale banks overnight, their edge in technology is a threat. Institutions need to improve their credit liquidity, reduce cost and, critically, use their data better in order to keep in step with organisations who were built from the ground up on data.

Banks, asset managers, insurers and credit funds are seeing significant return on investment in AI due to their ability to automate cumbersome and costly extraction of data from text-heavy documents.

For example, the wholesale banking division of one of our clients, headquartered in Europe, has used Eigen’s document AI technology to identify credit agreements referencing the LIBOR interest rate benchmark. As the bank’s back book extends to many thousands of loans and each agreement needed to be reviewed, this would have taken on average almost 70 minutes to complete manually.

Using AI technology to answer specific questions from loan documents to identify which ones needed attention, the bank was able significantly to shorten this exercise, resulting in a time saving of 75% and an estimated reduction in cost of 60%. 

Using AI to automate this sort of work will soon be something no financial institution can afford not to do, due to an onslaught of compliance-related work. But as AI becomes embedded in the way institutions work, it also has the potential to liberate employees from the burden of time-consuming manual work and also support efficiencies which lead to improved competitiveness.