Asian Journal of Criminal Justice and Forensic Studies

  • Received 16.01.2026,
  • Revised 04.05.2026,
  • Accepted 11.06.2026
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Vol. 2, No. 1, 2026
  • evidence; data; on-chain; assets; effectiveness; route
  • https://doi.org/10.63621/ajcjfs/1.2026.76
  • Pages 76-88

The aim of the study was to determine the level of effectiveness of digital forensics of virtual assets in law enforcement practices in India and Vietnam. The methodology was based on the conceptual-methodological method, regulatory and legal analysis, scenario-typological classification, case-study, and structural-logical modelling. It has been established that assessing the effectiveness of blockchain forensics should be based not only on on-chain analysis tools, but also on the complete operational chain of actions of the authority (artifact collection → on-chain analysis → identification of intermediaries → procedural actions → international cooperation → evidence formalisation). The effectiveness of blockchain forensics was operationalised through the metrics of asset restraint/recovery, disruption, attribution leverage, evidentiary robustness, and time-to-intervention. The presence of Anti-Money Laundering mechanisms and procedural procedures was a key condition for the transition from technical tracing to property measures and proving in criminal proceedings. It has been observed that in Indian practice, the main emphasis was placed on property results, which allowed for the seizure of assets worth approximately 1,646 crore rupees in the BitConnect case and the freezing of over 77 BTC in the E-Nuggets case. The success of investigations was ensured by a rapid transition from transaction analysis to interaction with exchanges, which effectively restricts criminals’ access to crypto assets. Vietnamese investigations focus on large-scale investment pyramids, where losses amounted to nearly 10 trillion dong and over 51 million USDT. Effectiveness in these cases was manifested through the complete shutdown of fraudulent platforms and the detention of organisers, achieved by combining on-chain payments with digital traces in accounts and logs. Evidentiary robustness requires formalised procedures concerning the data source, reproducibility, and the chain of custody, as well as a standardised sequence of actions: “on-chain tracing → establishment of a control point (Virtual Asset Service Provider / device) → procedural action”. The practical significance lies in the implementation of the results in the activities of law enforcement agencies, the judiciary, and the educational process to enhance the effectiveness of combating offences in the digital sphere

References

  1. Acharya, B., & Holz, T. (2024). An explorative study of pig butchering scams. ArXiv. doi: 10.48550/arXiv.2412.15423.
  2. Act of the Information Technology No. 21. (2000, June). Retrieved from https://www.indiacode.nic.in/bitstream/123456789/13116/1/it_act_2000_updated.pdf.
  3. Anti-Money Laundering Law No. 14/2022/QH15. (2022, November). Retrieved from https://vanban.chinhphu.vn/?classid=1&docid=207710&pageid=27160&typegroupid=.
  4. Arnone, G., Scire’, G., & Bivona, E. (2025). The (mis)use of cryptocurrencies by criminal organizations: A systematic literature review. Digital Finance, 7, 815-851. doi: 10.1007/s42521-025-00148-1.
  5. Atlam, H.F., Ekuri, N., Azad, M.A., & Lallie, H.S. (2024). Blockchain forensics: A systematic literature review of techniques, applications, challenges, and future directions. Electronics, 13(17), article number 3568. doi: 10.3390/electronics13173568.
  6. Bhattarai, A., Sahin, A., Veksler, M., Kurt, A., Aras, D., Imery, C., & Akkaya, K. (2025). Cryptocurrency forensics automation: A deep learning and NLP-based approach for mobile platforms. Discover Computing, 28, article number 123. doi: 10.1007/s10791-025-09595-1.
  7. Carpentier-Desjardins, C., Paquet-Clouston, M., Kitzler, S., & Haslhofer, B. (2025). Mapping the DeFi crime landscape: An evidence-based picture. Journal of Cybersecurity, 11(1), article number tyae029. doi: 10.1093/cybsec/tyae029.
  8. Castro Severiche, K.E., Wahlqvist Odenman, A., & Jalali, A. (2025). Ponzi scheme detection and prevention in blockchain platforms using machine learning: A systematic literature review. In P. Delir Haghighi, M. Greguš, G. Kotsis & I. Khalil (Eds.), Information integration and web intelligence. Lecture notes in computer science (Vol. 15342, pp. 87-102). Cham: Springer. doi: 10.1007/978-3-031-78090-5_8.
  9. CoinDesk. (2025). Retrieved from https://www.coindesk.com/policy/2025/02/17/india-s-directorate-of-enforcement-seizes-usd190m-in-bitconnect-fraud-case.
  10. Criminal Procedure Code No. 101/2015/QH13. (2015, November). Retrieved from https://vanban.chinhphu.vn/default.aspx?docid=183217&pageid=27160.
  11. Dudani, S., Baggili, I., Raymond, D., & Marchany, R. (2023). The current state of cryptocurrency forensics. Forensic Science International: Digital Investigation, 46, article number 301576. doi: 10.1016/j.fsidi.2023.301576.
  12. Ghosh, K., & Das, P.K. (2025). Comprehensive analysis of cryptocurrency, virtual digital assets, and distributed ledger technology with insights into Indian policies and research trends. Discover Analytics, 3, article number 3. doi: 10.1007/s44257-025-00030-9.
  13. Halder, D., & Saiyed, A.A. (2022). Legal challenges to cryptocurrency and its guardian-less victims in India: A critical victimological analysis. International Annals of Criminology, 60(1), 79-98. doi: 10.1017/cri.2022.6.
  14. Ho Chi Minh City Public Security Department. (2021). Retrieved from https://congan.hochiminhcity.gov.vn/wps/portal/Home/trang-chu/noi-dung-chi-tiet/tin-chuyen-nganh/tin%20hoat%20dong/catp%20triet%20pha%20san%20giao%20dich%20tien%20ao%20lua%20dao.
  15. India Today. (2022). Retrieved from https://www.indiatoday.in/india/story/ed-freezes-crypto-assets-worth-crores-e-nuggets-mobile-app-case-2005876-2022-09-28.
  16. Ji, S., Huang, C., Chu, H., Wang, X., Dong, H., & Zhang, P. (2024). Blockchain scam detection: State-of-the-art, challenges, and future directions. In J. Chen, B. Wen & T. Chen (Eds.), Blockchain and trustworthy systems. BlockSys 2023. Communications in computer and information science (Vol. 1896, pp. 3-18). Singapore: Springer. doi: 10.1007/978-981-99-8101-4_1.
  17. Kanezashi, H., Suzumura, T., Liu, X., & Hirofuchi, T. (2022). Ethereum fraud detection with heterogeneous graph neural networks. ArXiv. doi: 10.48550/arXiv.2203.12363.
  18. Law on Cybersecurity No. 24/2018/QH14. (2018, June). Retrieved from https://vanban.chinhphu.vn/?docid=206114&pageid=27160.
  19. Luong, H.T., & Ngo, H.M. (2024). Understanding the nature of the transnational scam-related fraud: Challenges and solutions from Vietnam’s perspective. Laws, 13(6), article number 70. doi: 10.3390/laws13060070.
  20. Ministry of Public Security. (2025a). Retrieved from https://mps.gov.vn/bai-viet/bat-khan-cap-nhom-doi-tuong-lap-san-giao-dich-tien-ao-loi-keo-nguoi-choi-voi-so-tien-dau-tu-gan-10-ngan-ty-dong-d22-t45318.
  21. Ministry of Public Security. (2025b). Retrieved from https://mps.gov.vn/bai-viet/cong-an-dak-lak-triet-xoa-duong-day-lua-dao-1-275-ty-dong-1766313157.
  22. Nguyen, N.T., Nguyen, A.T., To, H.T.N., & Le, T.T.H. (2023). Why were Vietnamese people susceptible to cryptocurrency Ponzi schemes? Findings from using the PLS-SEM approach. Journal of Financial Crime, 31(1), 158-173. doi: 10.1108/JFC-12-2022-0299.
  23. Nguyen, T.T.T. (2025). Digital evidence in criminal procedure: Legal challenges and solutions in Vietnam. Binh Duong University Journal of Science and Technology, 8(1). doi: 10.56097/binhduonguniversityjournalofscienceandtechnology.v8i1.297.
  24. Prakash, F., & Sadawarti, H. (2022). Blockchain-based chain of custody: A secure digital evidence framework for digital forensics investigation. Applied Innovative Research, 3(1-4), 108-112.
  25. Sakshi, Malik, A., & Sharma, A.K. (2023). Blockchain-based digital chain of custody multimedia evidence preservation framework for internet-of-things. Journal of Information Security and Applications, 77, article number 103579. doi: 10.1016/j.jisa.2023.103579.
  26. Saniyazova, Y., Mediyev, R., Saitova, E., Utegenova, G., & Kzylkhojayeva, A. (2024). Advancing forensic science in Kazakhstan: The emergence and impact of digital forensics in cybercrime investigation. Law, State and Telecommunications Review, 16(2), 48-68. doi: 10.26512/lstr.v16i2.49190.
  27. Seerwani, P., & Ram Mohan, M.P. (2025). Virtual digital assets service providers under Indian insolvency framework (Working Paper No. 2025-10-01). Ahmedabad: Indian Institute of Management Ahmedabad.
  28. Shaisultanov, S., Akimzhanov, T., Abdrakhmanov, B., Bazarlinova, A., & Bazarlinova, A. (2024). Combating internet fraud through operative-search measures. Law, State and Telecommunications Review, 16(2), 257-275. doi: 10.26512/lstr.v16i2.50740.
  29. Simbayev, T. (2025). Legal issues of non-conviction-based confiscation of digital assets obtained through criminal means. Law Journal, 3(3), 88-114. doi: 10.47344/cmay9446.
  30. The Prevention of Money-Laundering Act No. 15. (2003, January). Retrieved from https://www.indiacode.nic.in/handle/123456789/2036?sam_handle=123456789%2F1362.
  31. Trozze, A., Kamps, J., Akartuna, E.A., Hetzel, F.J., Kleinberg, B., Davies, T., & Johnson, S.D. (2022). Cryptocurrencies and future financial crime: A systematic review. Crime Science, 11(1), article number 1. doi: 10.1186/s40163-021-00163-8.
  32. Tsai, F.-C. (2021). The application of blockchain of custody in criminal investigation process. Procedia Computer Science, 192, 2779-2788. doi: 10.1016/j.procs.2021.09.048.
  33. Turner, A.B., McCombie, S., & Uhlmann, A.J. (2020). Discerning payment patterns in Bitcoin from ransomware attacks. Journal of Money Laundering Control, 23(3), 545-589. doi: 10.1108/JMLC-02-2020-0012.

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