Explainable Machine Learning for Credit Risk Management When Features are Dependent

Complex Machine Learning (ML) models used to support decision-making in peer-to-peer (P2P) lending often lack clear, accurate, and interpretable explanations. While the game-theoretic concept of Shapley values and its computationally efficient variant Kernel SHAP may be employed for this aim, similarly to other existing methods, the latter makes the assumption that the features are independent. The assumption of uncorrelated features in credit risk management is fairly restrictive and, thus, prediction explanations coming from correlated features might result in highly misleading Shapley values, even when considering simple models. We therefore propose an evaluation of different dependent-feature estimation methods of Kernel SHAP for classification purposes in credit risk management. We show that dependent-feature estimation of Shapley values can improve the understanding of true prediction explanations, their robustness and is essential for better identifying the most relevant variables to default predictions coming from black-box ML models. We propose estimation of feature-dependent Shapley values for P2P credit risk managementWe consider different linear and non-linear predictive models with varying degrees of dependenceDependent feature estimation of Shapley values can improve prediction explanations and their robustnessLoan amount and interest rate are the most determinant features to loan default prediction explanations