PRIVACY-PRESERVING EXPLAINABLE MODELS FOR EARLY DETECTION OF BANK FAILURES

Abstract

This research investigates the obstacles associated with achieving a balance between privacy and explainability in the prediction of bank failures by employing a differentially private glass-box technique. Although accurate early warning systems are essential for maintaining financial stability, private banking data is often used by these systems. Traditional black-box models have a lot of power, but regulators don't trust them because they're hard to understand. However, models that are easy to understand yet could reveal private information are known as glass-box models. This includes rule-based classifiers and decision trees. To solve for this trade-off, the suggested design uses differential privacy techniques in understandable models. Integrating calibrated noise during model training, the approach formally guarantees privacy without sacrificing prediction accuracy. This approach helps those with a stake in the matter, such banks and regulators, understand the key risk factors that impact the patterns of bank failure forecasts. In terms of accuracy, experimental assessments show that privacy-preserving glass-box models can hold their own against non-private alternatives. The framework's robustness in the face of different privacy budgets is further evidence of its practical utility. Being honest and open when making large financial decisions is vital, as stated in the report. It provides a way for the legitimate and moral application of AI in financial analytics as well.