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The research aims to develop a machine learning framework for accurately assessing credit risk while ensuring interpretability and fairness.

April 14, 2026Open Access

An Interpretable and Fair Machine Learning Framework for Credit Risk Assessment: Predicting Repayment Probability and Timing

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This framework predicts repayment probability and timing in credit risk assessment, suggesting improved transparency and fairness in finance.

The research aims to develop a machine learning framework for accurately assessing credit risk while ensuring interpretability and fairness.
Utilized the Lending Club and UCI Credit datasets for analysis.
Applied XGBoost for estimating repayment probabilities.
Employed SHAP values for model interpretation of decision-making.
Adopted a Cox Proportional Hazards model to analyze repayment timings.
Evaluated fairness metrics across different demographic groups.
XGBoost achieved a predictive accuracy of 94.94% and an AUC of 0.987.
The Cox model obtained a concordance index of 0.92, indicating effective repayment timeline prediction.
Fairness evaluation metrics showed responsible and transparent decision-making across demographic groups.

Chirag Dahal (2026) studied this question.

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This framework predicts repayment probability and timing in credit risk assessment, suggesting improved transparency and fairness in finance.