Approaches to risk assessment and management are changing with the introduction of machine learning, but is finance ready for the changes?
There has been a radical alteration in the nature of risk in many sectors, in that the biggest threat that many companies now face is from disruption of their business model by start-ups.
This has meant that traditional approaches to risk—the appointment of a risk officer, and identification of individual risks to be mitigated—are no longer sufficient. Risk now requires a whole-company approach.
In the financial services sector, however, ‘risk assessment and management’ still largely means assessing and managing individual credit risks. The regulatory requirements for this have been strengthened following the 2008 financial crisis, and one of the biggest challenges is to quantify the risks from digitization.
Machine learning has a part to play in this, but there is a real question about whether regulators are ready for these new approaches to risk management.
Is the Financial Sector Ready for Machine Learning?
Machine learning should provide significant advantages for risk management in the financial sector. It may, for example, help to open up opportunities to improve safety and security. Better algorithms should lead to more reliable credit decisions, based on relevant data, and hopefully not affected by human Machine learning error.
With such large quantities of data now available, it is asking a lot to expect any individual person to look at all of it, but a machine learning system will make light work of the analytics required.
Use of machine learning should also increase the efficiency of the risk and credit assessment process, by improving the models used. This, in turn, will speed up decisions, and therefore improve customer experience. It is not just that better models improve assessment on an individual basis, either.
Better credit risk management leads to improvements in financial institutions’ exposure to poor risks. This, in turn, is better for the whole financial system, increasing trust and improving ratings.
Related: Is Machine Learning the Same as AI?