INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS) ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XV January 2025 | Special Issue on Economics Page 130 www.rsisinternational.org Financial Intelligence Units and Regulatory Technology to Combat Crypto Laundering Mehedi Hassan Murad 1 , Imran Uddin 2 , Sadia Maliha Trisha 3 , Shuvo Kumar Mallik 4 , Md. Kabirul Islam 5 , Noushin Akhter Nova 6 1 Department of Finance and Banking, National University, Dhaka, Bangladesh 2 A2Z Finance Australia (Easy Mortgage Solutions Australia), Australia. 3 Dublin Business School, Dublin, Ireland. 4 Department of Economics, Southeast University, Dhaka, Bangladesh 5 Lecturer, Department of Finance and Banking, Uttara Town College, Dhaka, Bangladesh 6 Corporation Advisory, International Finance Corporation (IFC) DOI: https://dx.doi.org/10.47772/IJRISS.2025.915EC0010 Received: 30 January 2025; Accepted: 08 February 2025; Published: 20 February 2025 ABSTRACT Since digital payments increased in 2015, crypto laundering has become a significant threat. This study explores preventing crypto laundering through RegTech and FIU, using qualitative content analysis with NVivo 12. Legal documents from the Commodity Futures Trading Regulatory Agency (CFTR) served as secondary data sources. Crypto laundering mitigation includes KYC (Know Your Customer) and risk-based transaction monitoring. Customized RegTech solutions, like facial recognition technology, should complement blockchain analytics tools. The study found that government agencies issue suspicious transaction reports, while INTRAC, Indonesia's financial institution unit, handles transaction monitoring and analysis using a "follow the money" methodology. This research contributes to forensic accounting knowledge and suggests policy-level actions for regulators, collaborating with technology specialists to combat crypto laundering. Keyword: Crypto Laundering; Blockchain; RegTech; Financial Intelligence Unit INTRODUCTION Money laundering (ML) is an intangible process that conceals the source of profits earned from illegal activities (Gottschalk, 2010). It strengthens the application of ML which refers to blocking the profit of the illegal commitment by the offender (Pontes et al., 2022). In arithmetic, the predicate crime (fraud, corruption, and theft) undergoes the process of concealment (Pickett & Pickett, 2002; Larkin, S. B. 2025) and conversion process (Albrecht et al., 2012); thus, ML has been an important activity in financial crime (Gottschalk, 2010). ML activities are designed to facilitate the use of ‘illicit funds’ legally by the criminal (Gottschalk, 2010); hence the proceeds of those activities are integrated with lawful economic processes as the objective of ML is to convert unlawful proceeds into lawful ones. Therefore, in relation to this typology and ecosystem, the nature of the anti- money laundering (AML) system itself should be responsive and preventive of various methods of ML that occur, given that human lifestyles and technological advances influence the very dynamics of activities such as ML (Wronka, 2022a). Technological innovations, digitalization and the internet penetration have significantly influenced the operations of ML perpetrators steal digitalization technologies that provide new opportunities to commit ML with the most modern techniques (Mugarura & Ssali, 2020) (online transactions (cyber laundering) and use digital payments and virtual currencies. Money laundering using virtual currencies allows avoiding detection of law enforcement officials and complicating identification (Wronka, 2022a; Wronka, 2022b; Mardiansyah, 2021). Cyber laundering is supposed to make the detection quite hard as it operates in more than one currency