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Case Study
Credit Card Fraud Detection Using Random Forest and XG boost
Dr. Sumithra Devi K A1
G Pragna2
Pallavi V S3
Raaja Nithila Nethran4
Varsha V5
1Dean Academics and Head, Computer Science Engineering in Data Science, Dayananda Sagar Academy of Technology and Management, Bengaluru, Karnataka, India. 2345 Students, Department of Computer Science Engineering in Data Science, Dayananda Sagar Academy of Technology and Management, Bengaluru, Karnataka, India.
Published Online: May-August 2025
Pages: 266-270
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250402036
References
1. Improved Chen, T., and Guestrin, C., "XG Boost: A Scalable Tree Boosting System," Proceedings of the 22nd ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining, pp. 785-794, 2016.
2. Chawla, N.V., Bowyer, K.W., Hall, L.O., and Kegelmeyer, W.P., "SMOTE: Synthetic Minority Over-sampling Technique," Journal of
Artificial Intelligence Research, vol. 16, pp. 321-357, 2002.
3. Pedregosa, F., et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
4. Bishop, C.M., "Pattern Recognition and Machine Learning," Information Science and Statistics, Springer, 2006.
5. Fraud Detection in Credit Cards using Logistic Regression Hala Z Alenzi1 , Nojood O Aljehane.
6. Hala Z Alenzi, Nojood O Aljehane. (2020). "Fraud Detection in Credit Cards using Logistic Regression." International Journal of
Advanced Computer Science and Applications (IJACSA),Vol. 11, No. 12, 2020
7. Chidinma Faith Onyeoma, Husnain Rafiq, Daniel Jeremiah, Vinh Thong Ta, Muhammad Usman. (2024). "Credit Card Fraud Detection
Using Deep Neural Network With Shapley Additive Explanations." Edge Hill University, Ormskirk, UK
8. Xuetong Niu, Li Wang, Xulei Yang. (2019). "A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised."
Association for the Advancement of Artificial Intelligence (AAAI), 2019
9. Siyaxolisa Kabane. (2024). "Impact of Sampling Techniques and Data Leakage on XG Boost Performance in Credit Card Fraud
Detection." University of Fort Hare
10. Devi Meenakshi. B, Janani. B, Gayathri. S, Indira. N. (2019). "Credit Card Fraud Detection Using Random Forest." International
Research Journal of Engineering and Technology (IRJET), Volume 06, Issue 03, March 2019
11. Fanrui Zhang. (2023). "Improved credit card fraud detection method based on XG Boost algorithm." BCP Business & Management
EMFRM 2022, Volume 38, 2023
12. Sorin-Ionuț Mihali, Ștefania-Loredana Niță. (2024). "Credit Card Fraud Detection based on Random Forest Model." 17th International
Conference on Development and Application Systems, Suceava, Romania, May 23-25, 2024
13. Hastie, T., Tibshirani, R., and Friedman, J., "The Elements of Statistical Learning: Data Mining, Inference, and Prediction," Springer
Series in Statistics, 2nd Edition, 2009.
14. Breiman, L., "Random Forests," Machine Learning, vol. 45, pp. 5-32, 2
International Conference on Knowledge Discovery and Data Mining, pp. 785-794, 2016.
2. Chawla, N.V., Bowyer, K.W., Hall, L.O., and Kegelmeyer, W.P., "SMOTE: Synthetic Minority Over-sampling Technique," Journal of
Artificial Intelligence Research, vol. 16, pp. 321-357, 2002.
3. Pedregosa, F., et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
4. Bishop, C.M., "Pattern Recognition and Machine Learning," Information Science and Statistics, Springer, 2006.
5. Fraud Detection in Credit Cards using Logistic Regression Hala Z Alenzi1 , Nojood O Aljehane.
6. Hala Z Alenzi, Nojood O Aljehane. (2020). "Fraud Detection in Credit Cards using Logistic Regression." International Journal of
Advanced Computer Science and Applications (IJACSA),Vol. 11, No. 12, 2020
7. Chidinma Faith Onyeoma, Husnain Rafiq, Daniel Jeremiah, Vinh Thong Ta, Muhammad Usman. (2024). "Credit Card Fraud Detection
Using Deep Neural Network With Shapley Additive Explanations." Edge Hill University, Ormskirk, UK
8. Xuetong Niu, Li Wang, Xulei Yang. (2019). "A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised."
Association for the Advancement of Artificial Intelligence (AAAI), 2019
9. Siyaxolisa Kabane. (2024). "Impact of Sampling Techniques and Data Leakage on XG Boost Performance in Credit Card Fraud
Detection." University of Fort Hare
10. Devi Meenakshi. B, Janani. B, Gayathri. S, Indira. N. (2019). "Credit Card Fraud Detection Using Random Forest." International
Research Journal of Engineering and Technology (IRJET), Volume 06, Issue 03, March 2019
11. Fanrui Zhang. (2023). "Improved credit card fraud detection method based on XG Boost algorithm." BCP Business & Management
EMFRM 2022, Volume 38, 2023
12. Sorin-Ionuț Mihali, Ștefania-Loredana Niță. (2024). "Credit Card Fraud Detection based on Random Forest Model." 17th International
Conference on Development and Application Systems, Suceava, Romania, May 23-25, 2024
13. Hastie, T., Tibshirani, R., and Friedman, J., "The Elements of Statistical Learning: Data Mining, Inference, and Prediction," Springer
Series in Statistics, 2nd Edition, 2009.
14. Breiman, L., "Random Forests," Machine Learning, vol. 45, pp. 5-32, 2
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