Subject. This article discusses the use of modern machine learning methods to identify suspicious transactions and prevent financial losses in the banking sector. Objectives. The article aims to conduct a comparative analysis of modern machine learning methods used to identify fraudulent transactions with credit cards in order to identify the most effective approaches that can be implemented in the practice of banks to improve the accuracy and automation of the fraud detection process. Methods. The proposed methodology involves comparing various machine learning algorithms to select the most effective one to ensure the accuracy and reliability of anti-fraud systems for the purpose of automated detection of fraudulent credit card transactions. The study examined key machine learning algorithms used in fraud detection tasks, such as neural networks and Random Forest, as well as pre-processing and class balancing methods. The models were trained and tested on real banking data obtained from the Kaggle platform and including 30 predictors and more than 25,000 records. The effectiveness of the algorithms was assessed based on metrics such as AUC-ROC curves. Results. The results of the study show that algorithms based on artificial neural networks and the Random Forest algorithm provide high accuracy in detecting fraudulent transactions. It is found that the combination of these methods with various data pre-processing techniques can significantly increase the effectiveness of anti-fraud systems. In particular, the use of SMOTE significantly improves the accuracy of predictions for the minority class. Relevance. The proposed methodology can be successfully used in the banking sector to increase the reliability of anti-fraud systems. The results obtained may be useful for further improving automated fraud detection systems and minimizing financial losses associated with fraudulent transactions.
Klimov V.V., Shchukin B.A. [Modern technologies for identifying fraudulent transactions carried out using bank cards]. Finansovyi biznes = Financial Business, 2015, no. 3, pp. 25–30. (In Russ.) URL: Link
Maes S., Tuyls K., Vanschoenwinkel B., Manderick B. Credit Card Fraud Detection using Bayesian and Neural Networks: Proc. of the 1st International NAISO Congress on Neuro Fuzzy Technologies, 2002. URL: Link
Yazici Y. Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods, 2020. URL: Link
Beketnova Yu.M. [Comparative analysis of machine learning methods in identifying signs of involvement of credit institutions and their clients in dubious operations]. Finansy: teoriya i praktika = Finance: Theory and Practice, 2021, vol. 25, no. 5, pp. 186–199. (In Russ.) URL: Link
Breiman L. Random Forests. Statistics Department University of California, 2001, pp. 5–32. URL: Link
Aleskerov E., Freisleben B., Rao B. CARDWATCH: A Neural Network-Based Database Mining System for Credit Card Fraud Detection: Proc. of the IEEE/IAFE on Computational Intelligence for Financial Engineering, 1997, pp. 220–226. URL: Link
Bentley P., Kim J., Jung G., Choi J. Fuzzy Darwinian Detection of Credit Card Fraud: Proc. of 14th Annual Fall Symposium of the Korean Information Processing Society, 2000. URL: Link
Bolton R., Hand D. Unsupervised Profiling Methods for Fraud Detection, Credit Scoring and Credit Control VII. 2001. URL: Link
Brause R., Langsdorf T., Hepp M. Neural Data Mining for Credit Card Fraud Detection: Proc. of 11th IEEE International Conference on Tools with Artificial Intelligence, 1999. URL: Link
Chan P., Fan W., Prodromidis A., Stolfo S. Distributed Data Mining in Credit Card Fraud Detection. IEEE Intelligent Systems, 1999, vol. 14, pp. 67–74. URL: Link
Dorronsoro J., Ginel F., Sanchez C., Cruz C. Neural Fraud Detection in Credit Card Operations. IEEE Transactions on Neural Networks, 1997, vol. 8, pp. 827–834. URL: Link
Ezawa K., Norton S. Constructing Bayesian Networks to Predict Uncollectible Telecommunications Accounts. IEEE Expert, 1996, October, pp. 45–51. URL: Link
Kim M., Kim T. A Neural Classifier with Fraud Density Map for Effective Credit Card Fraud Detection: Proc. Of IDEAL, 2002, pp. 378–383. URL: Link
Kokkinaki A. On Atypical Database Transactions: Identification of Probable Frauds using Machine Learning for User Profiling: Proc. of IEEE Knowledge and Data Engineering Exchange Workshop, 1997, pp. 107–113. URL: Link
Brabazon A., Cahill J., Keenan P., Walsh D. Identifying Online Credit Card Fraud Using Artificial Immune Systems: IEEE Congress on Evolutionary Computation, 2010, pp. 1–7. URL: Link.
Roy A., Sun J., Mahoney R. et al. Deep Learning Detecting Fraud in Credit Card Transactions: Systems and Information Engineering Design Symposium (SIEDS), 2018, pp. 129–134. URL: Link
Fel'dman E.V., Ruchai A.N., Cherbadzhi D.Yu. [Model for identifying anomalous banking transactions based on machine learning]. Vestnik UrFO. Bezopasnost' v informatsionnoi sfere = Bulletin of the Urals Federal District. Security in the Information Sphere, 2021, no. 1, pp. 27–35. (In Russ.) URL: Link
Gupta J. Credit Card Fraud Detection Using Machine Learning Algorithms. International Journal of Science and Research, 2023. URL: Link
