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October 1, 2025Journal of Economics Finance and Accounting StudiesOpen Access

Explainable Artificial Intelligence for Credit Risk Assessment: Balancing Transparency and Predictive Performance

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Authors

MCM. A. ChoudhuryRZRasheed ZakariaNSN Sultana

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Overview

This research demonstrates how explainable artificial intelligence improves predictive performance in credit risk assessment, suggesting effective compliance and fairness in decisions.

Key Points

  • XAI integration improves transparency and predictive performance in credit risk assessment, enhancing trust.
  • The hybrid framework combines gradient boosting and neural networks with post-hoc interpretability tools SHAP and LIME.
  • Evaluating trade-offs on benchmark datasets shows that XAI methods maintain strong predictive accuracy while fostering fairness.
  • The findings support regulatory compliance and better risk management practices in financial decision-making.

Cite This Study

Choudhury et al. (2025) studied this question.

synapsesocial.com/papers/68dd89defe798ba2fc497b4bhttps://doi.org/10.32996/jefas.2025.7.6.2
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Credit Risk Modeling with Explainable AI: Predictive Approaches for Loan Default Reduction in Financial Institutions2021 · 17 citations
  2. 2Credit Risk Analysis using Explainable Artificial Intelligence2024 · 2 citations
  3. 3Explainable Artificial Intelligence Credit Risk Assessment using Machine Learning2025
  4. 4EXPLAINABLE AI IN MICROCREDIT RISK ASSESSMENT: BALANCING ACCURACY AND TRANSPARENCY2026
  5. 5Enhancing ML Models Interpretability for Credit Scoring2025