A recent paper from the Financial Stability Board considers the implications for artificial intelligence (AI) and machine learning in a number of financial services sectors, including credit risk.
The paper includes a useful section on background and definitions, and provides a clear reminder that these tools identify patterns and correlations rather than causality. I suspect that we will need to be reminded of this distinction more and more, as these tools are being used to explore complex relationships.
When it comes to credit risk scoring, the FSB is clear that AI may help to make lending decisions quicker. However, regulators are not persuaded that AI credit scoring models outperform traditional models – or at least, “it has not been proved”. For example, a recent paper from Moody’s compares the performance of their own credit scoring model for corporates against three machine learning approaches. Moody’s finds that, on average, the accuracy levels of the four models are comparable, and notes that the key to enhancing credit scoring models is data.
The FSB notes that the deployment of these AI tools would also allow access to credit to people or businesses whose creditworthiness cannot be reliably assessed through traditional credit scoring models. The FSB believes that this would be a positive development for countries with shallow credit markets (emerging markets?), though less positive for countries with deep credit markets (developed markets?). You have been warned…
Regulators are also concerned with the overall auditability of artificial intelligence models used for credit scoring and the wider impact on credit risk governance. There is an important dimension here about how the model is used in business. Is it operating with some human oversight? This is an important issue for business culture as it forces a consideration of who is ultimately in control. I suspect that the distinction between retail and commercial lending in terms of volume of transactions may become important; the volume of retail transactions might make human oversight more challenging.
Where does that leave the CEO, CFO or CRO of a bank contemplating the use of AI tools? Here are a few suggestions:
1. Have a shared view of the expected business outcomes from deploying AI tools.
2. Keep monitoring credit risk exposures and alignment with risk appetite even more intensively, as the AI tool might have unintended effects.
3. Focus on the auditability of the AI tool and its impact on credit risk governance.
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 Financial Stability Board, “Artificial intelligence and machine learning in financial services. Market developments and financial stability implications”, Nov 2017.
 Bacham, D and J. Y. Zhao, “Machine Learning: Challenges Lessons and Opportunities in Credit Risk Modelling”, Moody’s Analytics Risk Perspectives, July 2017.