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Machine Learning in Credit Risk: Measuring the Dilemma Between Predictions and Supervisory Cost
The financial sector is increasingly adopting machine learning (ML) tools to manage credit risk. In this environment, supervisors face the challenge of allowing credit institutions to benefit from technological progress and financial innovation, while at the same ensuring compatibility with regulatory requirements and that technological neutrality is observed. This paper proposes a framework for supervisors to measure the costs and benefits of evaluating ML models, aiming to shed more light on this technology’s alignment with the regulation. It then compute estimates the predictive gains of six ML models using a public database on credit defaults. It then calculates a supervisory cost function through a scorecard that assigns weights to each factor for each ML model, based on how the model is used by the financial institution and the supervisor’s risk tolerance.
