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

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.

Publication DOI: https://doi.org/10.3390/risks12110174
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies > Software Engineering & Cybersecurity
College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences
Aston University (General)
Additional Information: Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Uncontrolled Keywords: classification,credit risk,credit risk prediction,machine learning,Accounting,Economics, Econometrics and Finance (miscellaneous),Strategy and Management
Publication ISSN: 2227-9091
Data Access Statement: Data are available on https://www.kaggle.com/code/rikdifos/eda-vintage-analysis/data, accessed on 1 July 2024.
Last Modified: 01 Apr 2025 07:11
Date Deposited: 04 Dec 2024 08:21
Full Text Link:
Related URLs: https://www.mdp ... -9091/12/11/174 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-11
Published Online Date: 2024-11-04
Accepted Date: 2024-10-30
Authors: Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Sivakulasingam, Sharuga
Wang, Hai (ORCID Profile 0000-0002-4192-5363)
Wong, Siu Tung
Ganatra, Meghana Ashok
Luo, Jiabin (ORCID Profile 0000-0002-2599-2822)

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