A cross-border community for researchers with openness, equality and inclusion
Join USS Create Account Sign In
Join USS Create Account Sign In
Presentation details
  1. Home
  2. Presentation list
  3. Presentation details
Explainable AI-Driven Credit Risk Assessment and Financial Decision Support Using Random Forest, LIME, and SHAP
ID：72 View protection：Participant Only Updated time：2026-07-22 16:09:39 Views：24 Online

Start Time：2026-07-31 11:55

Duration：15min

Session：[S1] 5G and beyond Wireless Networks [S1-4] 5G and beyond Wireless Networks

Slide
Abstract
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.
Keywords
Credit risk assessment, explainable artificial intelligence, financial decision support, random forest, decision tree, LIME, SHAP, model governance.
Speaker
Musab Iqtait
zarqa university

Post comments
Verification Code Change Another
All comments
Important Dates

  • Conference date

    07-30

    2026

    -

    08-01

    2026

  • 07-28 2026

    Draft paper submission deadline

  • 07-28 2026

    Registration deadline

Sponsored By

The United Societies of Science

Organized By

Kongunadu College of Engineering and Technology

Contact info
Previous Conferences
请输入有效的邮箱地址
© Copyright 2024 USS - the United Societies of Science - All rights reserved. The USS website is developed and maintained by BoomSCI limited.
×

USS WeChat Official Account

USSsociety

Please scan the QR code to follow
the wechat official account.