Adopting ML for the Internal Capital Adequacy Assessment Process would change the way the industry and the markets assess bank credit risk. Market participants would find it difficult to discount outcomes of a capital allocation process largely driven by ML, especially when the accuracy and transparency of ML models become more evident. The complexity of ML and potential difficulties in interpreting and explaining outputs are challenges, but the benefit of that complexity is better predictive power.

Read the full report by Sam Theodore.