An Empirical Study on Credit Risk Assessment Using Machine Learning: Evidence from the Kaggle Credit Card Fraud Detection Dataset
DOI:
https://doi.org/10.71222/7mn4wp34Keywords:
credit risk assessment, fraud detection, machine learning, XGBoost, neural networks, AUC, imbalanced dateAbstract
This paper investigates machine learning approaches for assessing credit risk, with an emphasis on detecting fraud in credit card usage. Based on the Kaggle dataset, we compare models such as Decision Tree, Random Forest, SVM, Neural Networks, and XGBoost using metrics like Accuracy, Precision, Recall, F1-Score, and AUC. Results show that Random Forest and Neural Networks achieve high accuracy, while XGBoost and Neural Networks are more effective in identifying fraud, with better Recall and AUC. The study underlines challenges from imbalanced data and points out that methods like resampling and ensemble techniques are vital for improving detection. Future work should further enhance fraud detection by integrating deep learning and reinforcement learning methods.
References
1. L. Yun, “Analyzing Credit Risk Management in the Digital Age: Challenges and Solutions”, Econ. Manag. Innov., vol. 2, no. 2, pp. 81–92, Apr. 2025, doi: 10.71222/ps8sw070.
2. S. N. Kalid et al., "Detecting frauds and payment defaults on credit card data inherited with imbalanced class distribution and overlapping class problems: A systematic review," IEEE Access, vol. 12, pp. 23636–23652, 2024, doi: 10.1109/ACCESS.2024.3362831.
3. Z. Faraji, "A review of machine learning applications for credit card fraud detection with a case study," SEISENSE J. Manag., vol. 5, no. 1, pp. 49–59, 2022, doi: 10.33215/sjom.v5i1.770.
4. M. Chogugudza, "The classification performance of ensemble decision tree classifiers: A case study of detecting fraud in credit card transactions," Identifier, vol. vital 69317, 2022.
5. J. O. Awoyemi, A. O. Adetunmbi, and S. A. Oluwadare, "Credit card fraud detection using machine learning techniques: A comparative analysis," in 2017 Int. Conf. Comput. Netw. Informatics (ICCNI), IEEE, 2017, doi: 10.1109/ICCNI.2017.8123782.
6. A. R. Khalid et al., "Enhancing credit card fraud detection: an ensemble machine learning approach," Big Data Cogn. Comput., vol. 8, no. 1, p. 6, 2024, doi: 10.3390/bdcc8010006.
7. X. Feng and S.-K. Kim, "Novel machine learning based credit card fraud detection systems," Mathematics, vol. 12, no. 12, p. 1869, 2024, doi: 10.3390/math12121869.
8. E. Ileberi, Improved machine learning methods for enhanced credit card fraud detection, University of Johannesburg, South Africa, 2023.
9. D. Kadam, "Machine learning approaches to credit card fraud detection," Int. J. Res. Appl. Sci. Eng. Technol., 2024, doi: /10.22214/ijraset.2024.60531.
10. Y. K. Saheed, U. A. Baba, and M. A. Raji, "Big data analytics for credit card fraud detection using supervised machine learning models," in Big Data Analytics in the Insurance Market, Emerald Publishing Limited, 2022, pp. 31–56, doi: 10.1108/978-1-80262-637-720221003.
11. S. Yang, “The Impact of Continuous Integration and Continuous Delivery on Software Development Efficiency”, J. Comput. Signal Syst. Res., vol. 2, no. 3, pp. 59–68, Apr. 2025, doi: 10.71222/pzvfqm21.
12. M. Shanaa and S. Abdallah, "A hybrid anomaly detection framework combining supervised and unsupervised learning for credit card fraud detection," F1000Research, vol. 14, p. 664, 2025, doi: 10.12688/f1000research.166350.1.
13. F. Gao, “The Role of Data Analytics in Enhancing Digital Platform User Engagement and Retention”, J. Media Journal. Commun. Stud., vol. 1, no. 1, pp. 10–17, Apr. 2025, doi: 10.71222/z27xzp64.
14. L. Theodorakopoulos et al., “Credit Card Fraud Detection with Machine Learning and Big Data Analytics: A PySpark Framework Implementation,” Preprints, no. 202407.0022, Jul. 2024, doi: 10.20944/preprints202407.0022.v1.
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Copyright (c) 2025 Ruoyu Qi (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
