AN SMOTE-BASED METHOD FOR ENHANCING CREDIT CARD FRAUD DETECTION

Credit card fraud occurs most often in online purchases; therefore, it is crucial to employ better ways to find it to avoid financial loss. This paper discusses fraud detection by employing methods to generate synthetic data to improve detection models. We use the Kaggle credit card transaction dataset, implementing synthetic data generation using SMOTE as a way to balance the dataset, in which fraud cases comprise only 0.2% of cases, and perform feature engineering to better understand buying behavior. We experimented with five ML models–XGBoost, LightGBM, Random Forest, Neural Networks, and Logistic Regression; focusing on precision, recall, F1-score, and accuracy. The comparison indicates that XGBoost achieves its highest F1-score (82.57%) with good precision (93.75%) and recall (73.77%), indicating XGBoost can balance false positives and false negatives. Although all models performed with high accuracy (over 99.9%), this research focuses on highlighting precision and recall in fraud detection. The findings suggest that combining synthetic data with gradient-boosting algorithms can help fraud detection systems improve the security of online purchases.

1. Dorfleitner, G., and Jahnes, K. Banking fraud: Global financial impact and detection methodologies. Journal of Financial Crime, 29 (2), 456–471 (2022).

2. Khanum, A., Chaitra, K.S., Singh, B., and Gomathi, C. Fraud detection in financial transactions: A machine learning approach vs. rule-based systems. Proceedings of the 2nd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024 (2024).

3. Rani, S., and Mittal, A. Securing digital payments a comprehensive analysis of ai driven fraud detection with real time transaction monitoring and anomaly detection. Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, 2345–2349 (2023).

4. Peneti, S., Krishna, S.R., Kiran, A., and Tripathy, H.K. Credit card fraud detection using machine learning. Proceedings of 2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024 (2024).

5. Thar, K.W., and Wai, T.T. Machine learning based predictive modelling for fraud detection in digital banking. Proceedings of the 21st IEEE International Conference on Computer Applications 2024, ICCA 2024, 249–253 (2024).

6. Ahirwar, N., Singh, D., and Maheshwar, K. Efficient credit card fraud detection based on multiple ml algorithms. 2024 IEEE 9th International Conference for Convergence in Technology (I2CT), 1–7 (2024).

7. Ruchita, M., Bhargavi, M., Rakshita, M., Nandini, B.C., Aziz, I., and Gopi, J. Leveraging smote and random forest for improved credit card fraud detection. 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA), 795–800 (2024).

8. Anusha, P., Bharath, S., Rajendran, N., Durga Devi, S., and Saravanakumar, S. Experimental evaluation of smart credit card fraud detection system using intelligent learning scheme. 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 1–6 (2023).

9. Singh, A., Gill, K.S., Kumar, M., and Rawat, R. Beyond traditional methods: Evaluating advanced machine learning models for superior fraud detection. 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS), 297–300 (2024).

10. Khalid, A.R., Owoh, N., Uthmani, O., Ashawa, M., Osamor, J., and Adejoh, J. Enhancing credit card fraud detection: An ensemble machine learning approach. Big Data and Cognitive Computing, 8 (1), 6 (2024).

11. Axenie, C., Tudoran, R., Bortoli, S., Al Hajj Hassan, M., Salort Sanchez, C., and Brasche, G. Dimensionality reduction for low-latency high-throughput fraud detection on datastreams. 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 1170–1177 (2019).

12. Reddy Basani, M.A. Data engineering and ml for real-time fraud detection in financial transactions. 2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2), 91–96 (2024).

13. Mohapatra, A., Kumar, A., Kumar, B., Agarwal, H., and Priyadarshini, R. Synthetic data generation and handling data imbalance for mobile financial transactions. CSNT (2024).

14. Sultana, S., Rahman, M.S., and Afroj, M. An efficient fraud detection mechanism based on machine learning and blockchain technology. 2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2023, 162–168 (2023).