用可解释AI提升信贷风险预测透明度，模型准确率超90%。
Explainable Artificial Intelligence Credit Risk Assessment using Machine Learning
- 融合XGBoost、LightGBM和随机森林，结合SHAP/LIME增强可解释性。
- LightGBM在准确率和审批/违约平衡上表现最优，AUC达0.92以上。
- 生成个人化解释报告，适合金融风控与合规审查场景使用。
本文提出一种智能且透明的AI信贷风险评估系统，采用XGBoost、LightGBM和随机森林三种先进集成学习模型，结合可解释人工智能（XAI）技术进行贷款违约风险预测。预处理包括自定义插补、独热编码和标准化；通过SMOTE处理类别不平衡问题，并使用GridSearchCV进行超参数调优。模型在多个指标上评估，包括ROC-AUC、精确率、召回率和F1分数。LightGBM表现最佳，具有最高准确率及审批与违约率之间的最优权衡。系统还生成申请人专属的XAI可视化报告和业务影响摘要，确保决策透明。实验表明，该系统在真实信贷数据集上达到AUC超过0.92，具备实际部署价值。
原文摘要 · Abstract (English)
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.
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