Affiliation:
1. Department of Cyber Security and Networks, Glasgow Caledonian University, Glasgow G4 0BA, UK
2. Department of Software Engineering, African University of Science and Technology, Abuja 900107, Nigeria
Abstract
In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as credit card fraud techniques continue to evolve.
Reference40 articles.
1. Sahithi, G.L., Roshmi, V., Sameera, Y.V., and Pradeepini, G. (2022, January 28–30). Credit Card Fraud Detection using Ensemble Methods in Machine Learning. Proceedings of the 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India.
2. Federal Trade Commission (2023, March 11). CSN-Data-Book-2022. no. February 2023, Available online: https://www.ftc.gov/system/files/ftc_gov/pdf/CSN-Data-Book-2022.pdf.
3. UK Finance (2023, November 20). Annual Report and Financial Statements 2022. Available online: https://www.ukfinance.org.uk/annual-reports.
4. Unbalanced Credit Card Fraud Detection Data: A Machine Learning-Oriented Comparative Study of Balancing Techniques;Gupta;Procedia Comput. Sci.,2023
5. Mondal, I.A., Haque, M.E., Hassan, A.-M., and Shatabda, S. (2021, January 18–20). Handling imbalanced data for credit card fraud detection. Proceedings of the 2021 24th International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh.
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