Banks apply machine learning to CCAR models
ML models benchmarked against traditional iterations to avoid ‘black box’ perception
Banks are increasingly seeking to apply machine-learning techniques to the models they use for regulatory stress tests.
Machine-learning algorithms – designed to quickly make sense of large, unstructured datasets – are already used by banks to validate the models built for the US Federal Reserve’s Comprehensive Capital Analysis and Review (CCAR). Here, dealers use the technology to develop challenger models that act as checks on the primary models used to project how a bank will fare under
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