Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring

• 成果类型: Article; Early Access
• 署名作者: Hue, Sullivan; Hurlin, Christophe; Perignon, Christophe; Saurin, Sebastien
• 署名单位: Aix-Marseille Universite; Centre National de la Recherche Scientifique (CNRS); Universite de Orleans; Institut Universitaire de France; Hautes Etudes Commerciales (HEC) Paris
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.02025
• 发表日期: 2026
• 关键词: explainability; Credit scoring; performance metrics; Shapley values

摘要

Because they play an increasingly important role in determining access to credit, credit scoring models are under growing scrutiny from banking supervisors and internal model validators. These authorities need to monitor the model performance and identify its key drivers. To facilitate this, we introduce the explainable performance (XPER) methodology to decompose a performance metric (e.g., area under the curve (AUC), R2) into specific contributions associated with the various features of a forecasting model. XPER is theoretically grounded on Shapley values and is both model-agnostic and performance metric-agnostic. Furthermore, it can be implemented either at the model level or at the individual level. Using a novel data set of car loans, we decompose the AUC of a machine-learning model trained to forecast the default probability of loan applicants. We show that a small number of features can explain a surprisingly large part of the model performance. Notably, the features that contribute the most to the predictive performance of the model may not be the ones that contribute the most to individual forecasts (Shapley additive explanation). Finally, we show how XPER can be used to deal with heterogeneity issues and improve performance.