Developing Explainable AI Scorecards for Retail Credit Risk: A Reproducible Framework Using Lending Data

This article presents a step‑by‑step framework for developing an interpretable scorecard for retail credit‑risk assessment in banks and financial institutions, and validates the approach on a fully synthetic consumer‑lending dataset. The model is an integration of Weight‑of‑Evidence & Information Value (WoE & IV) binning, machine learning algorithm – Lasso (L1) Regularized Logistic Regression, model‑risk supervision, statistical metrics such as Roc-auc & Gini and Confusion Matrix. The testing dataset demonstrates ROC‑AUC of 86% and a 72% Gini uplift generalization over a rule‑based benchmark. The final result is a ready credit scorecard illustrating the distribution of points assigned to the background features of a customer applying for a loan.