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<p>As a result of the traditional credit assessment method’s reliance on the financial analyst’s experience and the fact that the majority of the information comes from the debtor, lending institutions are currently faced with a significant issue in predicting credit risk. Machine learning algorithms were therefore developed to ascertain whether a borrower was qualified to seek a loan and committed to paying installments. In this research, a potential effort is made to apply feature engineering techniques, such as missing data correction, data transformation, feature selection, resampling unbalanced target class, and also comparing prediction accuracy of the most prominent ensemble machine learning models (i.e. XGBoostC, LGBMC, CatBoostC) that have special interest in the classification of credit scoring by using Berhan bank’s ten years credit dataset. The dataset has over 47,000 loan records and 16 features with personal, loan, and collateral information. To split the dataset for training and testing, the StratifiedKFold algorithm is used with tenfold cross-validation. Confusion matrix and AUC-ROC metrics are used to evaluate the performance of machine learning models. Finally, the outcome demonstrates that the outperformer ensemble model, CatBoost Classifier, has superior prediction accuracy in terms of AUC = 87% and confusion matrix = 90% metrics.</p>

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Sustainable Credit Risk Prediction: Applying Machine Learning for Responsible Lending

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月20日

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  • Fitsum Tibebe Araya,
  • Beakal Gizachew Assefa,
  • Sudhir Kumar Mohapatra,
  • Ch Biswaranjan Nanda

摘要

As a result of the traditional credit assessment method’s reliance on the financial analyst’s experience and the fact that the majority of the information comes from the debtor, lending institutions are currently faced with a significant issue in predicting credit risk. Machine learning algorithms were therefore developed to ascertain whether a borrower was qualified to seek a loan and committed to paying installments. In this research, a potential effort is made to apply feature engineering techniques, such as missing data correction, data transformation, feature selection, resampling unbalanced target class, and also comparing prediction accuracy of the most prominent ensemble machine learning models (i.e. XGBoostC, LGBMC, CatBoostC) that have special interest in the classification of credit scoring by using Berhan bank’s ten years credit dataset. The dataset has over 47,000 loan records and 16 features with personal, loan, and collateral information. To split the dataset for training and testing, the StratifiedKFold algorithm is used with tenfold cross-validation. Confusion matrix and AUC-ROC metrics are used to evaluate the performance of machine learning models. Finally, the outcome demonstrates that the outperformer ensemble model, CatBoost Classifier, has superior prediction accuracy in terms of AUC = 87% and confusion matrix = 90% metrics.