Explainable Artificial Intelligence Credit Risk Assessment using Machine Learning

TL;DR

This paper develops an explainable AI system for credit risk assessment that combines ensemble machine learning models with XAI techniques to improve transparency and decision-making in loan approvals.

Contribution

It introduces a novel integrated system using XGBoost, LightGBM, and Random Forest with SHAP and LIME for interpretability, tailored for credit risk analysis.

Findings

LightGBM achieved the highest accuracy and optimal trade-offs.

The system provides applicant-specific visual explanations.

Model performance was validated with multiple metrics.

Abstract

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.