A strategy for protecting a digital service from fraudsters attempting to launder illegally obtained income

   Relevance. Money laundering through digital platforms, including cryptocurrencies and decentralized financial services, poses a serious threat to the financial system. Attackers use cross-chain bridges and mixer wallets to disguise the origin of funds, making it difficult to track them. This creates challenges for regulators and financial institutions, requiring the development of new counteraction methods.

   The purpose is to develop an effective strategy to combat money laundering on digital platforms.

   Objectives: to analyze existing fraud detection methods; identify their shortcomings; propose ways to eliminate them and empirically verify the effectiveness of the new strategy.

   Methodology. The dialectical method, analysis and synthesis are used. The research is based on the study of scientific and economic literature. The application of machine learning and artificial intelligence for monitoring transactions in real time and detecting anomalies is considered. Special attention is paid to strengthening KYC (Know Your Customer) and AML (Anti-Money Laundering) systems. A statistical experiment was conducted to confirm the hypothesis.

   The results of the research and experiment show that the introduction of integrated analytical systems based on the analysis of big data and user behavior can contribute to more effective detection and prevention of financial crimes. An important element of the strategy is active interaction between the components of the protection system, such as identification, verification and monitoring of user behavior.

   Conclusions. The integration of advanced technologies and cooperation with regulators make it possible to minimize risks and ensure compliance with international standards. Continuous technology improvement is necessary to adapt to new threats in the digital economy.

1. Vendor Providing Crypto Transaction Monitoring Services, Annual Report. ChainAnalysis. (In Russ.) Available at: https://www.chainalysis.com/wp-content/uploads/2024/06/the-2024-crypto-crime-report-release.pdf (accessed 04. 07. 2025).

2. Rathore M.M., Chaurasia S., Shukla D. Mixers detection in bitcoin network: A step towards detecting money laundering in crypto-currencies. In: 2022 IEEE International Conference on Big Data (Big Data). Osaka, Jaban: IEEE; 2022. P. 5775-5782. doi: 10.1109/bigdata55660.2022.10020982

3. Hilal W., Gadsden S.A., Yawney J. Financial fraud : A review of anomaly detection techniques and recent advances Expert Systems with Applications. 2022;193:118. doi: 10.1016/j.eswa.2021.116429

4. Afjal M., Salamzadeh A., Dana L.-P. Financial fraud and credit risk: Illicit practices and their impact on banking stability. Journal of Risk and Financial Management. 2023;16(9):386. doi: 10.3390/jrfm16090386

5. Crawford J., Guan Y. Knowing your bitcoin customer: Money laundering in the Bitcoin economy. In: 2020 13^th International Conference on Systematic Approaches to Digital Forensic Engineering (SADFE). New York, USA: IEEE; 2020. P. 38-45. doi: 10.1109/sadfe51007.2020.00013

6. Boreiko D. Initial coin offerings pitfalls: Scams, flops, and security breaches. In: Understanding Initial Coin Offerings: A New Era of Decentralized Finance (Understanding series). Cheltenham, United Kingdom: Edward Elgar Publishing. 2024. P. 176-200. doi: 10.4337/9781803921587.00014

7. Hornuf L., Kück T., Schwienbacher A. Initial coin offerings, information disclosure, and fraud. Small Business Economics. 2021;58(4):1741-1759. doi: 10.1007/s11187-021-00471-y

8. Yusoff Y.H., et al. Roles of auditor in combating money laundering: A concept paper. International Journal of Academic Research in Business and Social Sciences. 2023;13(4):78-87. doi: 10.6007/ijarbss/v13-i4/16593

9. Han J., et al. Artificial Intelligence for anti-money laundering : A review and extension. Digital Finance. 2020;2(3-4):211-239. doi: 10.1007/s42521-020-00023-1

10. Sidorov A.L. The main stages of user identity verification during registration in digital platforms. In: Aktual'nye problemy infotelekommunikatsii v nauke i obrazovanii : sbornik nauchnykh statei Mezhdunarodnoi nauchno-tekhnicheskoi i nauchno-metodicheskoi konferentsii = Actual problems of infotelec communications in science and education : Collection of scientific articles of the International scientific, technical and scientific-methodical conference. Vol. 4. St. Petersburg: Sankt-Peterburgskii gosudarstvennyi universitet telekommunikatsii imeni professora M.A. Bonch‒Bruevicha; 2023. P. 128-132. (In Russ.)

11. Bello O., Olufemi K. Artificial intelligence in fraud prevention: Exploring techniques and applications challenges and opportunities. Computer Science & IT Research Journal. 2024;5(6):1505–1520. doi: 10.51594/csitrj.v5i6.1252

12. Skinner C.P. Coins, cross-border payments, and anti-money laundering law. Harvard Journal on Legislation. 2023;60:285.

13. Gupta A., Dwivedi D.N., Shah J. Overview of money laundering. In: Artificial Intelligence Applications in Banking and Financial Services. Singapore: Springer; 2023. P. 1-11. doi: 10.1007/978-981-99-2571-1_1

14. Levi M., Reuter P. Money laundering. Crime and Justice. 2006;34(1):289-375. doi: 10.1086/501508

15. Gotelaere S., Paoli L. Prevention and control of financial fraud : A scoping review. European Journal on Criminal Policy and Research. 2022;31:1-21. doi: 10.1007/s10610-022-09532-8

16. Cox D.W. Handbook of anti-money laundering. Chichester, West Sussex: Wiley; 2014. 754 p.

17. Wronka C. Cyber-laundering: The change of money laundering in the Digital age. Journal of Money Laundering Control. 2021;25(2):330-344. doi: 10.1108/jmlc-04-2021-0035

18. Wang H.-M., Hsieh M.-L. Cryptocurrency is new vogue: A reflection on money laundering prevention. Security Journal. 2023;37(1):25-46. doi: 10.1057/s41284-023-00366-5

19. Dubey A., Choubey S. Blockchain and machine learning for data analytics, privacy preserving, and security in fraud detection. i-manager’s Journal on Software Engineering. 2023;18(1):45. doi: 10.26634/jse.18.1.20091

20. Lim J.W. A facilitative model for cryptocurrency regulation in Singapore. In: Handbook of Digital Currency. Academic Press; 2024. P. 341-361. doi: 10.1016/b978-0-323-98973-2.00023-x

21. Aprilia G. F. Exploring detection and prevention of money laundering with information technology. Journal of Money Laundering Control. 2024;27(6):995-1004.

22. Dharmavaram V.G., Mishra O. KYC Fraud: A New Means to Conduct Financial Fraud – How to Tackle. In: Cybersecurity Issues, Challenges, and Solutions in the Business World. Global Scientific Publishing; 2022. P. 81-94. doi: 10.4018/978-1-6684-5827-3.ch006

23. Sohel A., Alam M.A., Waliullah M., Siddiki A., Uddin M.M. Fraud detection in financial transactions through data science for real-time monitoring and prevention. Academic Journal on Innovation, Engineering & Emerging Technology. 2024;1(1):91-107.

24. Javaid H.A. How Artificial Intelligence is Revolutionizing Fraud Detection in Financial Services. Innovative Engineering Sciences Journal. 2024;4(1):4.

25. Shoetan P., Familoni B. Transforming Fintech fraud detection with advanced artificial intelligence algorithms. Finance & Accounting Research Journal. 2024;6(4):602-625. doi: 10.51594/farj.v6i4.1036