发表机构
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CredWise是一个集成预测、校准、可解释AI、策略检索与SQL分析的受控智能体框架，用于可解释可审计的信用风险评估，实验表明其各组件性能优异，但最终决策仍由人工完成。
AI 中文摘要
信用风险预测在银行业中十分重要，但仅凭预测本身并不能解释申请人为何具有风险，也无法说明应如何将其与其他证据相结合。本文提出了CredWise，一个决策支持框架，整合了信用风险预测、概率校准、可解释人工智能、策略检索、SQL分析以及受控的智能体工作流。使用时间划分方式，在Lending Club数据（1,345,310笔贷款，18个特征）上训练了一个XGBoost模型：2007年至2016年用于训练，2017年用于验证，2018年用于测试。在2018年测试集上，校准后的模型取得了ROC-AUC为0.7109、PR-AUC为0.2993、F1得分为0.3714以及准确率为65.44%的结果。校准将Brier得分从0.2157降至0.1273，并将预期校准误差从0.2862降至0.0585。SHAP解释在时间上保持稳定，2017年和2018年特征排序之间的Spearman相关系数为0.9959。在覆盖九个策略部分的28个标注查询上，FAISS取得了最佳Hit@1（0.929）和MRR（0.964），而所有三种检索方法的Hit@5均达到1.0。智能体路由实现了95.6%的准确率（45个案例中43个正确），SQL基准在六个案例中的精确匹配、执行成功和结果匹配方面均获得1.0的得分。这些结果表明，CredWise能够在单一受控工作流中结合预测、解释、策略证据和结构化分析。它是一个学术研究原型，最终决策仍由人工审核员作出。
英文摘要
Credit-risk prediction is important in banking, but a prediction alone does not explain why an applicant is risky or how it should be combined with other evidence. This paper presents CredWise, a decision-support framework that integrates credit-risk prediction, probability calibration, explainable artificial intelligence, policy retrieval, SQL analytics, and controlled agent-based workflows. An XGBoost model is trained on Lending Club data (1,345,310 loans, 18 features) using a temporal split: 2007--2016 for training, 2017 for validation, and 2018 for testing. On the 2018 test set, the calibrated model achieved a ROC-AUC of 0.7109, PR-AUC of 0.2993, F1-score of 0.3714, and accuracy of 65.44\%. Calibration reduced the Brier score from 0.2157 to 0.1273 and the expected calibration error from 0.2862 to 0.0585. SHAP explanations were temporally stable, with a Spearman correlation of 0.9959 between 2017 and 2018 feature rankings. On 28 labeled queries covering nine policy sections, FAISS achieved the best Hit@1 (0.929) and MRR (0.964), while all three retrieval methods reached Hit@5 = 1.0. Agent routing achieved 95.6\% accuracy (43 of 45 cases), and the SQL benchmark scored 1.0 on exact-match, execution-success, and result-match across six cases. These results show that CredWise can combine predictions, explanations, policy evidence, and structured analytics in one controlled workflow. It is an academic research prototype, and final decisions remain with a human reviewer.
