Is Finance Ready for Machine Learning?

Credit risk is not the only concern in the financial services world. Rogue traders and malpractice have cost banks and other financial institutions significant amounts in fines and reputational damage.

Start-up Behavox is using machine learning systems to examine behavior of employees and compare it with those who have previously ‘gone bad’, with a view to identifying potential ‘rogues’ in advance of any problems, and therefore mitigating this risk. The system relies on data from financial institutions, so will improve as more institutions sign up, but there are already signs that quite a number of organisations see the potential benefits.

Understanding the challenges

There are, however, also some challenges to using machine learning to create models for risk management. We have already mentioned one: are regulators ready for this step? Indeed, are financial institutions? The appetite is certainly there among many, because of the potential efficiencies. However, many lack understanding of the precise systems required. Those who are not already working on this area will need to start on it soon, or risk being left behind by the competition.

Because a machine learning system learns for itself, there is a danger that analytical models to assess risk become a ‘black box’, and nobody is quite sure exactly how decisions are being made. This lack of transparency may not be acceptable to either regulators or customers, although there is a moot point about whether a human decision is actually any more transparent.

Perhaps this is simply a matter of how the technology and models are presented to stakeholders. At the same time, of course, there is a bigger problem, that a model that nobody understands is in danger of becoming unreliable. “Computer says no” has already become something of a standing joke in credit decisions.

It may be that machine learning for risk management will become standard as organisations are forced to look more closely at their data to generate additional value and increase efficiency. It may, in other words, come as part of overall digitization efforts, rather than specifically for risk management.

Skills challenges

Perhaps one of the biggest challenges for any organisation in using machine learning will be skills shortages in a number of areas. These may well include lack of change leadership skills, because a move to this type of system is likely to involve cultural change as well as new algorithms.

Organisations using machine learning need to make their approach transparent and visible to all, with machine learning becoming part of ‘how we do business’.

In my view, only this will create the right ethic across the company. If you want to have a clear view of how machine learning brings opportunities and challenges to organizations, read the white paper The Evolution of Analytics.

This article has been republished with permission from SAS.