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Explainable AI-Driven Credit Risk Assessment and Financial Decision Support Using Random Forest, LIME, and SHAP
编号：72 访问权限：仅限参会人 更新：2026-07-22 16:09:39 浏览：42次 Online

报告开始：2026年07月31日 11:55（Asia/Kolkata）

报告时间：15min

所在会场：[S1] 5G and beyond Wireless Networks [S1-4] 5G and beyond Wireless Networks

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摘要
Credit-risk assessment is a high-impact financial task, as automated loan decisions influence portfolio quality, institutional profitability, regulatory compliance, and customer access to credit. Machine learning models can increase the predictive power, but black-box predictions are hard to justify in regulated financial settings. In this paper, we propose a novel explainable artificial intelligence (XAI) decision-support framework for credit-risk assessment, based on decision tree and random forest classifiers combined with Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). This approach extends the original experimental design with explicit steps of preprocessing, imbalance treatment, model training, evaluation and explanation. Results are discussed in terms of accuracy, precision, recall, and F1-score, as well as, behaviors of the confusion matrix, feature importance analysis, explanations at the local applicant level, and a governance-oriented financial interpretation. The enhanced framework demonstrates how XAI can transform a predictive credit model into a transparent, auditable and policy-relevant decision support system for banks, credit officers, risk committees and borrowers.
关键词
Credit risk assessment, explainable artificial intelligence, financial decision support, random forest, decision tree, LIME, SHAP, model governance.
报告人
Musab Iqtait
Assistant Professor zarqa university

稿件作者
Musab Iqtait zarqa university
jafar ababneh zarqa university
Hattar Hattar Zarqa University
Raad Almagdadi mut'ah university
Maan Alrawadiah Mutah University
AbdAlrahman Aljboor Mutah University
Shaher Alshabatat Mutah University
Amer Abu Orab Mutah University
Talal Alawamleh Mutah University
Mohamed Hafez INTI-IU-University;Shinawatra University
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重要日期

  • 会议日期

    07月30日

    2026

    至

    08月01日

    2026

  • 07月28日 2026

    初稿截稿日期

  • 08月03日 2026

    注册截止日期

主办单位
The United Societies of Science
承办单位
Kongunadu College of Engineering and Technology
协办单位
IEEE Section
IEEE Madras Section
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