Explainable Artificial Intelligence Credit Risk Assessment using Machine Learning
This addresses the problem of interpretability in credit risk assessment for financial institutions, but it is incremental as it applies existing methods to a specific domain.
The paper tackled credit risk assessment by developing an AI system that combines ensemble machine learning models with explainable AI techniques, resulting in LightGBM achieving the highest accuracy and optimal trade-off between approval and default rates.
This paper presents an intelligent and transparent AI-driven system for Credit Risk Assessment using three state-of-the-art ensemble machine learning models combined with Explainable AI (XAI) techniques. The system leverages XGBoost, LightGBM, and Random Forest algorithms for predictive analysis of loan default risks, addressing the challenges of model interpretability using SHAP and LIME. Preprocessing steps include custom imputation, one-hot encoding, and standardization. Class imbalance is managed using SMOTE, and hyperparameter tuning is performed with GridSearchCV. The model is evaluated on multiple performance metrics including ROC-AUC, precision, recall, and F1-score. LightGBM emerges as the most business-optimal model with the highest accuracy and best trade off between approval and default rates. Furthermore, the system generates applicant-specific XAI visual reports and business impact summaries to ensure transparent decision-making.