Crafting an Efficient Credit Risk Alert System: Assessment and Validation of the WOE-Enhanced Logistic Regression Model

In the current development landscape of the credit industry, risk management faces a series of challenges. Although technological advancements have brought significant progress to this field, issues such as high labor costs and insufficient customer authentication persist, highlighting the urgent need to build efficient risk prediction models. Taking the GiveMeSomeCredit dataset on the Kaggle platform as an example, this study applies feature-engineering techniques to develop a debt default early warning model aimed at identifying potential credit risks in advance. By combining in-depth optimization of IV and WOE values, logistic regression models and an LR-WOE model were constructed. Comprehensive evaluation using metrics such as PSI, KS statistic, and AUC scores ensured the robustness of the models' risk prediction accuracy. The research findings reveal: (1) Family structure plays a crucial role in credit risk assessment. Applicants with 0 or 7 dependents exhibit a higher probability of default compared to the overall sample, while those with 6 or 8 dependents demonstrate relatively lower default risk. (2) The constructed LR-WOE model performed the best, indicating its effectiveness in distinguishing borrowers with different credit profiles and maintaining stable predictive performance across various thresholds. Integrating WOE transformation techniques with logistic regression models can help financial institutions assess credit risks more accurately and optimize risk management strategies.