DIGITAL TRANSFORMATION OF PAYMENT SYSTEMS: FRAUD ISSUES AND DETECTION PROSPECTS
Abstract and keywords
Abstract (English):
The COVID-19 pandemic boosted digitalization in all areas of human activity, including finances. As a result, the problem of credit card fraud is particularly relevant today. This comprehensive analysis highlights the latest and most relevant developments in the detection of credit card fraud, e.g., artificial intelligence, big data processing, and cloud computing methods. It focuses on the evolution of payment systems, including the shift from traditional methods to innovative solutions based on IoT devices and biometric data. The existing security systems remain vulnerable and require novel fraud detection tools and methods. The modern approaches to transaction data processing include distributed computing and machine learning, which proved effective in the context of dynamically changing users’ behavior patterns. Diverse data sources are needed to improve the accuracy of fraud detection. Cloud technologies can create systems capable of prompt response to new types of fraud in real time. Promising research directions include hybrid models based on data from IoT devices and biometric indicators.

Keywords:
fraud, bank cards, digital transformation, digital payments, tokenization, biometric systems, transactions, artificial intelligence, Internet of things (IoT)
Text
Text (PDF): Read Download
References

1. Liu W., Wang X., Peng W. State of the art: Secure mobile payment. IEEE Access, 2020, 8: 13898–13914. https://doi.org/10.1109/ACCESS.2019.2963480

2. Sumina A. V. Possibilities of artificial intelligence in the detection, investigation, and prevention of crimes. Actual problems of law and law enforcement activity: Regional aspects: Proc. All-Russian Sci.-Prac. Conf., Krasnodar, 27 Oct 2023. Krasnodar: Krasnodar University of the MIA of Russia, 2024, 179–181. (In Russ.) https://elibrary.ru/yxcvqr

3. Sumina A. V. Introduction of information technology in law enforcement: The possibilities of artificial intelligence in the detection, investigation, and prevention of crime. Information technology in the activities of internal affairs: Proc. Intern. Sci.-Prac. Conf., Moscow, 18 Apr 2024. Moscow: V. Y. Kikotya Moscow University of the MIA of the Russian Federation, 2024, 266–268. (In Russ.) https://elibrary.ru/hwnxbv

4. Vasilieva Y. D., Sumina A. V. Artificial intelligence in law enforcement: Potential applications for crime detection and investigation. Law, society, and state: Problems of history, theory, and practice: Proc. All-Russian Sci.-Prac. Conf., Moscow, 12 Apr 2024. Staroteryaevo: V. Y. Kikotya Moscow University of the MIA of Russia, 2024, 547–551. (In Russ.) https://elibrary.ru/qmfidx

5. Abduragimova T. I. Disclosure and investigation of manufacturing, sale, and use of counterfeit credit and settlement plastic cards. Cand. Law Sci. Diss. Moscow, 2001, 201. (In Russ.) https://elibrary.ru/nntouz

6. Suchkova E. A. Investigation of the identity of the perpetrator in the course of investigation of illegal circulation of means of payment. Scientific Bulletin of the Orel Law Institute of the Ministry of the Interior of Russia named after V. V. Lukyanov, 2024, (3): 258–265. (In Russ.) https://elibrary.ru/cvztfo

7. Filippov M. N. Technique of investigation of thefts and frauds, committed with the use of credit cards of bank details. Vedomosti penal-executive system, 2015, (5): 26–30. (In Russ.) https://elibrary.ru/uygmnj

8. Meshcheryakov V. A. Theoretical foundations of the mechanism of trace formation in digital forensics. Moscow: Prospect, 2022, 176. (In Russ.) https://elibrary.ru/ejfpkb

9. Al Hashedi K. G., Magalingam P. Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019. Computer Science Review, 2021, 40. https://doi.org/10.1016/j.cosrev.2021.100402

10. Yvan Lucas, Johannes Jurgovsky. Credit card fraud detection using machine learning: A survey. arXiv, 2020. https://doi.org/10.48550/arXiv.2010.06479

11. Popat R., Chaudhary J. A survey on credit card fraud detection using machine learning. 2018 2nd International conference on trends in electronics and informatics (ICOEI): Proc. Conf., Tirunelveli, 11–12 May 2018. IEEE, 2018, 1120–1125. https://doi.org/10.1109/ICOEI.2018.8553963

12. Kanika, Singla J. A survey of deep learning based online transactions fraud detection systems. 2020 International conference on intelligent engineering and management (ICIEM): Proc. Conf., London, 17–19 Jun 2020. IEEE, 2020, 130–136. https://doi.org/10.1109/ICIEM48762.2020.9160200

13. Mittal S., Tyagi S. Computational techniques for real-time credit card fraud detection. Handbook of computer networks and cyber security, eds. Gupta B., Perez G., Agrawal D., Gupta D. Cham: Springer, 2020. https://doi.org/10.1007/978-3-030-22277-2_26

14. Singh A., Jain A. An empirical study of AML approach for credit card fraud detection–financial transactions. International Journal of Computers Communications & Control, 2019, 14(6): 670–690. https://doi.org/10.15837/ijccc.2019.6.3498

15. DiGabriele J., Heitger L., Riley R. A synthesis of non-fraud forensic accounting research. Journal of Forensic Accounting Research, 2020, 5(1): 257–277. https://doi.org/10.2308/JFAR-19-034

16. Hossein Motlagh N. Near field communication (NFC) – A technical overview. Dr. Diss. 2012, 73. https://doi.org/10.13140/RG.2.1.1232.0720

17. Vishwakarma P. P., Tripathy A. K., Vemuru S. Fraud detection in NFC-enabled mobile payments: A comparative analysis. Innovative data communication technologies and application, eds. Raj J. S., Iliyasu A. M., Bestak R., Baig Z. A. Singapore: Springer, 2021, vol. 59, 397–403. https://doi.org/10.1007/978-981-15-9651-3_34

18. Pasquet M., Gerbaix S. The complexity of security studies in NFC payment system. Australian information security management conference: Proc. 8 Conf., Perth, 30 Nov 2010. Pert: Cowan University, 2010, 95–101. https://doi.org/10.4225/75/57b674cb34783

19. Lukinsky I. S., Gorsheneva I. A. Promt engineering in the educational process and scientific activity or to the question of the necessity of training to work with artificial intelligence. Psychology and pedagogy of service activity, 2024, (4): 148–154. (In Russ.) https://doi.org/10.24412/2658-638X-2024-4-148-154

20. Mironchuk V. A., Zolkin A. L., Batishchev A. V., Urusova A. B. Integration of big data and analytical capabilities into modern decision support systems. Vestnik of the Academy of Knowledge, 2023, (5): 227–230. (In Russ.) https://elibrary.ru/decrqv

21. Doko F., Miskovski I. An overview of big data analytics in banking: Approaches, challenges and issues. UBT international conference. 2019, 11–17. URL: https://knowledgecenter.ubt-uni.net/conference/2019/events/270 (accessed 3 May 2025).

22. Laney D. 3D data management: Controlling data volume, velocity and variety. META Group Research. 2001. URL: https://diegonogare.net/wp-content/uploads/2020/08/3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf (accessed 3 May 2025).

23. Schroeck M., Shockley R., Smart J. Analytics: The real-world use of big data: How innovative enterprises extract value from uncertain data, Executive Report. 2012. URL: https://www.researchgate.net/publication/315786855_Analytics_the_real-world_use_of_big_data_How_innovative_enterprises_extract_value_from_uncertain_data_Executive_Report (accessed 13 May 2025).

24. Gandomi A., Haider M. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 2015, 35(2): 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007

25. Jeffrey Dean, Sanjay Ghemawat. MapReduce: Simplified data processing on large clusters. Communications of the ACM, 2008, 51(1): 107– 113. https://doi.org/10.1145/1327452.1327492

26. Nawsher Khan, Ibrar Yaqoob, Ibrahim Abaker Targio Hashem, Zakira Inayat, Waleed Kamaleldin Mahmoud Ali, Muhammad Alam, Muhammad Shiraz, Abdullah Gani. Big Data: Survey, technologies, opportunities, and challenges. The Scientific World Journal, 2014, 2014(1): 1–18. https://doi.org/10.1155/2014/712826

27. Dean J., Ghemawat S. MapReduce: A flexible data processing tool. Communications of the ACM, 2019, 53(1): 72–77. https://doi.org/10.1145/1629175.1629198

28. Shvachko K. V., Kuang H., Radia S. R., Chansler R. J. The hadoop distributed file system. 2010 IEEE 26th Symposium on mass storage systems and technologies (MSST): Proc. Conf., Incline Village, NV, 3–7 May 2010. IEEE, 2010, 1–10. https://doi.org/10.1109/MSST.2010.5496972

29. Vavilapalli V. K., Murthy A. C., Douglas C., Agarwal S., Konar M., Evans R., Graves T., Lowe J., Shah H., Seth S., Saha B., Curino C., O’Malley O., Radia S., Reed B., Baldeschwieler E. Apache Hadoop YARN: Yet another resource negotiator. SOCC '13: ACMsymposium on cloud computing: Proc. conf., California, 1–3 Oct 2013. NY: Association for Computing Machinery, 2013, 1–16. https://doi.org/10.1145/2523616.2523633

30. Madhavi A., Sivaramireddy T. Real-Time credit card fraud detection using spark framework. Machine learning technologies and applications, eds. Kiran Mai C., Brahmananda Reddy A., Srujan Raju K. Springer, 2021, 287–298. https://doi.org/10.1007/978-981-33-4046-6_28

31. Zhou H., Sun G., Fu S., Wang L., Hu J., Gao Y. Internet financial fraud detection based on a distributed big data approach with Node2vec. IEEE Access, 2021, 9: 43378–43386. https://doi.org/10.1109/ACCESS.2021.3062467

32. Mironchuk V. A., Zolkin A. L., Meksheneva Zh. V., Poskryakov I. A. Modern computer decision support systems. Natural and Humanitarian Research, 2023, (4): 228–231. (In Russ.) https://elibrary.ru/ebeehk

33. Wischik D., Handley M., Braun M. B. The resource pooling principle. ACM SIGCOMM Computer Communication Review, 2008, 38(5): 47–52. https://doi.org/10.1145/1452335.1452342

34. Galante G., de Bona L. C. E. A survey on cloud computing elasticity. 2012 IEEE fifth international conference on utility and cloud computing: Proc. conf., Chicago, 5–8 Nov 2012. IEEE, 2012, 263–270. https://doi.org/10.1109/UCC.2012.30

35. Kumari P., Mishra S. P. Analysis of credit card fraud detection using fusion classifiers. Computational intelligence in data mining, eds. Behera H., Nayak J., Naik B., Abraham A. Singapore: Springer, 2019, vol. 711, 111–122. https://doi.org/10.1007/978-981-10-8055-5_11

36. Wiścicka-Fernando M. 2021. The use of mobile technologies in online shopping during the covid-19 pandemic – an empirical study. Procedia Computer Science, 2021, 192: 3413–3422. https://doi.org/10.1016/j.procs.2021.09.114

37. Lukinsky I. S. Typology of industrial revolutions and their classifications through the prism of innovations in the field of technical and forensic support. Vestnik Kemerovskogo gosudarstvennogo universiteta. Seriia: Gumanitarnye i obshchestvennye nauki, 2023, 7(4): 505–511. (In Russ.) https://doi.org/10.21603/2542-1840-2023-7-4-505-511


Login or Create
* Forgot password?