Credit Risk Prediction Using Machine Learning and Deep Learning: A Study on Credit Card Customers

Victor Chang (), Sharuga Sivakulasingam, Hai Wang, Siu Tung Wong, Meghana Ashok Ganatra and Jiabin Luo
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Victor Chang: Department of Operations and Information Management, Aston Business School, Aston University, Birmingham B4 7ET, UK
Sharuga Sivakulasingam: Department of Operations and Information Management, Aston Business School, Aston University, Birmingham B4 7ET, UK
Hai Wang: School of Computer Science and Digital Technologies, Aston University, Birmingham B4 7ET, UK
Siu Tung Wong: Institute of Finance and Technology, University College London, London WC1E 6BT, UK
Meghana Ashok Ganatra: Department of Operations and Information Management, Aston Business School, Aston University, Birmingham B4 7ET, UK
Jiabin Luo: Department of Operations and Information Management, Aston Business School, Aston University, Birmingham B4 7ET, UK

Risks, 2024, vol. 12, issue 11, 1-33

Abstract: The increasing population and emerging business opportunities have led to a rise in consumer spending. Consequently, global credit card companies, including banks and financial institutions, face the challenge of managing the associated credit risks. It is crucial for these institutions to accurately classify credit card customers as “good” or “bad” to minimize capital loss. This research investigates the approaches for predicting the default status of credit card customer via the application of various machine-learning models, including neural networks, logistic regression, AdaBoost, XGBoost, and LightGBM. Performance metrics such as accuracy, precision, recall, F1 score, ROC, and MCC for all these models are employed to compare the efficiency of the algorithms. The results indicate that XGBoost outperforms other models, achieving an accuracy of 99.4%. The outcomes from this study suggest that effective credit risk analysis would aid in informed lending decisions, and the application of machine-learning and deep-learning algorithms has significantly improved predictive accuracy in this domain.

Keywords: credit risk prediction; credit risk; classification; machine learning (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2024
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