More banks flirt with machine learning for CCAR—but risks persist
The superior computational grunt of neural networks is attractive to lenders, but a lack of explainability presents a significant downside.
Machine learning techniques are taking hold in US banks’ stress-testing models, bit by bit and byte by byte. Proponents trumpet their ability to calculate revenue and loan-loss forecasts faster than existing methods. But users are running up against a familiar barrier: the difficulty of explaining the complex practices to model validators and regulators.
One large US bank is developing a prototype model for its annual Comprehensive Capital Analysis and Review (CCAR) as well as for the Current
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