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An Explainable AI Model for Credit Evaluation and Loan Decision Support
ID：117 View Protection：ATTENDEE Updated Time：2026-07-22 16:10:08 Hits：12 Online

Start Time：2026-07-31 15:10（Asia/Kolkata）

Duration：15min

Session：S6 Artificial Intelligence Use Cases » S6-7Artificial Intelligence Use Cases

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Abstract
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.
 
Keywords
Index Terms— Explainable AI; Credit Risk; Loan Approval; Random Forest; LIME; SHAP; Partial Dependence Plot; Machine Learning in Finance.
Speaker
Jafar Ababneh
PhD researcher in in Jafar Ababneh Cyber Security department； Faculty of Information Technology Zarqa University Zarqa； Jordan jababneh@zu.edu.jo

Submission Author
Jafar Ababneh Jafar Ababneh Cyber Security department； Faculty of Information Technology Zarqa University Zarqa； Jordan jababneh@zu.edu.jo
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Important Date

  • Conference Date

    Jul 30

    2026

    to

    Aug 01

    2026

  • Jul 28 2026

    Registration deadline

  • Jul 30 2026

    Draft paper submission deadline

Sponsored By
The United Societies of Science
Organized By
Kongunadu College of Engineering and Technology
Supported By
IEEE Section
IEEE Madras Section
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