Interpretable Machine Learning Models for Credit Risk Assessment

For lending organizations, determining a borrower's creditworthiness is essential to determining their capacity to repay loans. The prediction of credit scores using feature engineering and machine learning techniques is the main emphasis of this study. Using the Kaggle Family Credit Default Ri...

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Bibliographic Details
Published in2024 11th International Conference on Computing for Sustainable Global Development (INDIACom) pp. 361 - 365
Main Authors Hassan, Md Al Mahedi, T, Rakshit Govind, Mansur, Usman Muhammad, Jha, Roshan, Fahim, Md Forkan Hossain, R, Mahesh T
Format Conference Proceeding
LanguageEnglish
Published Bharati Vidyapeeth, New Delhi 28.02.2024
Subjects
Computational modeling
credit risk prediction
LightGBM
Logistic regression
Machine learning
Machine learning algorithms
Stability criteria
Support vector machines
SVM
Transforms
XGBoost algorithm
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Summary:For lending organizations, determining a borrower's creditworthiness is essential to determining their capacity to repay loans. The prediction of credit scores using feature engineering and machine learning techniques is the main emphasis of this study. Using the Kaggle Family Credit Default Risk dataset, the AUC scores of several machine learning models are compared. Modern machine learning techniques, including well-established methods like Random Forest and Linear Support Vector Machines, can be effectively applied to credit scoring. Ensemble models, such as LightGBM, offer advantages like improved predictions and increased stability, making them well-suited for this specific use case. Combining predictions from multiple models often results in less noisy outcomes compared to using a single model, outperforming other techniques like XGBoost, SVMs, and logistic regression.
DOI:10.23919/INDIACom61295.2024.10498183