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An Explainable AI Model for Credit Evaluation and Loan Decision Support
编号：117 访问权限：仅限参会人 更新：2026-07-22 16:10:08 浏览：19次 Online

报告开始：2026年07月31日 15:10（Asia/Kolkata）

报告时间：15min

所在会场：[S6] Artificial Intelligence Use Cases [S6-7] Artificial Intelligence Use Cases

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摘要
Loan approval is a core function of banking because it impacts institutional profitability, credit accessibility, and financial stability. While artificial intelligence and machine-learning models can improve the speed and accuracy of credit evaluation, many high-performing models are difficult for customers, credit officers, and regulators to interpret, Such lack of transparency is problematic if an application is rejected as applicants need comprehensible reasons and lenders need to demonstrate consistency, fairness and policy compliance,. In this paper, an explainable artificial intelligence (XAI) framework for credit evaluation based on the Logistic Regression, Support Vector Machine, Decision Tree and Random Forest classifiers is proposed. We evaluate the framework on a public loan-approval dataset and explain it using LIME, SHAP and Partial Dependence Plots. Random Forest model gave the best reported performance with accuracy, sensitivity and specificity of 0.998, 0.998 and 0.997 respectively. The explanatory layer is useful in explaining local decisions for specific applicants as well as global features effects over the data set. The proposed framework offers a transparent, accountable, and customer-centric approach to credit decision-making. Prior to practical implementation, additional validation, fairness testing, leakage checks, and regulatory assessment are required.
 
关键词
Index Terms— Explainable AI; Credit Risk; Loan Approval; Random Forest; LIME; SHAP; Partial Dependence Plot; Machine Learning in Finance.
报告人
Jafar Ababneh
PhD researcher in in Jafar Ababneh Cyber Security department； Faculty of Information Technology Zarqa University Zarqa； Jordan jababneh@zu.edu.jo

稿件作者
Musab Iqtait Department of Information Technology, Smart College for Modern Education (SCME), Hebron, Palestine miqtait@gmail.com
Jafar Ababneh Jafar Ababneh Cyber Security department； Faculty of Information Technology Zarqa University Zarqa； Jordan jababneh@zu.edu.jo
Jamal Bani salamah Mutah University
Salah Alshamary Salah Alshamary Cyber Security department, Faculty of Information Technology Zarqa University Zarqa, Jordan
Jamal Alkhasawneh Mutah University
Hassan Al-Ababneh Zarqa University
Shaher Alshabatat 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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