October 2, 2025Open Access

Explainable Artificial Intelligence Credit Risk Assessment using Machine Learning

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This analysis demonstrates credit risk assessment using machine learning models, suggesting transparency in AI-driven decisions.

LightGBM provides the highest accuracy in assessing credit risks, optimizing the balance between approvals and defaults.
Performance metrics like ROC-AUC, precision, recall, and F1-score were evaluated to ensure model effectiveness and reliability.
Custom imputation and standardization were incorporated in data preprocessing to enhance model predictions while managing class imbalance.
Applicant-specific reports generated by the system enhance decision-making transparency, utilizing explainable AI techniques like SHAP and LIME.

Sharma et al. (2025) studied this question.

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October 2, 2025Open Access

View Full Paper

Ask AI

Bookmark

Authors

This analysis demonstrates credit risk assessment using machine learning models, suggesting transparency in AI-driven decisions.

LightGBM provides the highest accuracy in assessing credit risks, optimizing the balance between approvals and defaults.
Performance metrics like ROC-AUC, precision, recall, and F1-score were evaluated to ensure model effectiveness and reliability.
Custom imputation and standardization were incorporated in data preprocessing to enhance model predictions while managing class imbalance.
Applicant-specific reports generated by the system enhance decision-making transparency, utilizing explainable AI techniques like SHAP and LIME.

Sharma et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark