Rethinking model validation for GenAI governance
A US model risk leader outlines how banks can recalibrate existing supervisory standards
For more than two decades, model risk management (MRM) has been built on a simple but powerful assumption: given defined inputs, a model produces a stable and repeatable output. Whether validating a value-at-risk engine or an expected credit loss model, validators relied on backtesting, sensitivity analysis and benchmarking against an assumed mathematical ground truth.
Those techniques assume
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