Synchronization of AI and Deep Learning for Credit Card Fraud Detection


  • Irshad Nazeer, KDV Prasad, Promila Bahadur, Varsha Bapat, Kurian M. J.


Deep Learning, AI, Fraud Detection, Credit Card


At current scenario, more and more businesses are moving toward accepting credit card payments online, there is a growing demand for an efficient fraud detection solution that is able to send alerts in real time that can be acted upon. The banking and financial sector of a country is one of the most significant contributors to the growth and development of the economy of that country. In recent years, consumers have become increasingly reliant on credit and debit cards for all of their purchasing needs, whether they prefer to do their shopping online or in-store. Because of this, the number of people using bank cards has skyrocketed. As a result, the number of monetary exchanges completed with plastic has increased significantly. Customers & other organisations are all being put in a precarious position as a result of fraudulent actors in this situation. Internet banking has emerged as a significant channel for conducting business deals as a result of the widespread availability of more recent technological advancements. There is a significant trust and safety issue caused by fake activities and fraudulent transactions. This is a problem because fake banking activities and fraudulent transactions can be committed by anyone. Additionally, the proliferation of sophisticated frauds like virus infections, scams, and fake websites cause enormous losses due to fraudulent activities. These frauds are just some of the ways that fraudulent activities can result in enormous losses. All of these cons are examples of more sophisticated forms of fraud. This research makes three important contributions to the fight against fraudulent activity involving the use of credit cards.


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Author Biography

Irshad Nazeer, KDV Prasad, Promila Bahadur, Varsha Bapat, Kurian M. J.

1Dr. Irshad Nazeer, 2Dr. KDV Prasad, 3Dr. Promila Bahadur, 4Dr. Varsha Bapat, 5Dr. Kurian. M.J.

1Professor of MBA, Recognized Ph.D. Research Guide, Presidency Business School, Presidency College Re-accredited by NAAC with 'A+’, Kempapura, Hebbal  Bengaluru

2Assistant Professor (Research), Symbiosis Institute of Business Management (SIBM), Symbiosis International (Deemed University) (SIU) Off Bangalore Highway, Kothur Mandal Village: Mamidipally, Dist: Mahabubnagar, Hyderabad, Telangana

3Associate Professor, Department of Computer Science and Engineering, IET, Lucknow, Uttar Pradesh

4Associate Professor, Department of Electronic Science, PES'S Modern College of Arts, Science and Commerce, Pune, Maharashtra

5Associate Professor in Computer Applications, Baselios Poulose II Catholicos College, Piravom, Kerala



Achituve I, Kraus S, Goldberger J, “Interpretable Online Banking Fraud Detection Based on Hierarchical Attention Mechanism”, In proceedings of 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), pp.1-6, October 2019.

Bogaerts, B., Bontempi, G., Geurts, P., Harley, N., Lebichot, B., Lenaerts, T., & Louppe, G. (2021). Artificial Intelligence and Machine Learning.

Claessens J, Dem V, De Cock D, Preneel B, Vandewalle J, “On the security of today’s online electronic banking systems”, Computers & Security, vol.21, no.3, pp.253-65, June 2002.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

Dembla, N., & Arya, M. K. (2019). Effect of Machine Learning in E- Commerce. Kaav International Journal of Economics, Commerce & Business Management, 6(1), 298-305.

Kingma, D. P., & Welling, M. (2019). An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4), 307-392.

Lebichot, B., Le Borgne, Y. A., He-Guelton, L., Oblé, F., & Bontempi, G. (2020). Deep-learning domain adaptation techniques for credit cards fraud detection. In Recent Advances in Big Data and Deep Learning: Proceedings of the INNS Big Data and Deep Learning Conference INNSBDDL2019, held at Sestri Levante, Genova, Italy 16-18 April 2019 (pp. 78-88). Springer International Publishing.

Lebichot, B., & Saerens, M. (2020). An experimental study of graph-based semi-supervised classification with additional node information. Knowledge and Information Systems, 62(11), 4337-4371.

Taha A and Malebary S J, “An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine”, IEEE Access, vol.8, pp.25579-87, February 2020.

Revathi P, “Digital Banking Challenges and Opportunities in India”, EPRA International Journal of Economic and Business Review, vol.7, no.12, pp.20-3, 2019.

Srividya, R., & V, L. (2018). Artificial Intelligence in Hospitals. National Journal of Arts, Commerce & Scientific Research Review, 6(1), 335-338.

Srivastava, A., Srivastava, P., & Chaudhary, A. (2022). DigitaL Fraud (1st ed., pp. 11-18). Kaav Publications.

Sivakumar, N., Balasubramanian, R.: Fraud detection in credit card transactions: classification, risks and prevention techniques. International Journal of Computer Science and Information Technologies 6(2) (2015)

Wei W, Li J, Cao L, Ou Y, Chen J, “Effective detection of sophisticated online banking fraud on extremely imbalanced data”, World Wide Web, vol.16, no.4, pp.449-75, July 2013.

Wang, J.H., Liao, Y.L., Tsai, T.m., Hung, G.: Technology-based financial frauds in taiwan: issues and approaches. In: Systems, Man and Cybernetics, 2006. SMC’06. IEEE International Conference on. IEEE (2006),to%20traditional%20machine%20learning%20methods.

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How to Cite

Irshad Nazeer, KDV Prasad, Promila Bahadur, Varsha Bapat, Kurian M. J. (2023). Synchronization of AI and Deep Learning for Credit Card Fraud Detection. International Journal of Intelligent Systems and Applications in Engineering, 11(5s), 52–59. Retrieved from