A Systematic Review of Artificial Intelligence Applied to Compliance: Fraud Detection in Cryptocurrency Transactions

Leslie Rodríguez Valencia (), Maicol Jesús Ochoa Arellano, Santos Andrés Gutiérrez Figueroa, Carlos Mur Nuño, Borja Monsalve Piqueras, Ana del Valle Corrales Paredes, Sergio Bemposta Rosende, José Manuel López López, Enrique Puertas Sanz and Asaf Levi Alfaroviz
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Leslie Rodríguez Valencia: La Empresa del Futuro, Faculty of Economic, Business and Communication Sciences, Universidad Europea de Madrid, C. Tajo, s/n, 28670 Madrid, Spain
Maicol Jesús Ochoa Arellano: La Empresa del Futuro, Faculty of Economic, Business and Communication Sciences, Universidad Europea de Madrid, C. Tajo, s/n, 28670 Madrid, Spain
Santos Andrés Gutiérrez Figueroa: La Empresa del Futuro, Faculty of Economic, Business and Communication Sciences, Universidad Europea de Madrid, C. Tajo, s/n, 28670 Madrid, Spain
Carlos Mur Nuño: La Empresa del Futuro, Faculty of Economic, Business and Communication Sciences, Universidad Europea de Madrid, C. Tajo, s/n, 28670 Madrid, Spain
Borja Monsalve Piqueras: Inteligencia Artificial e Interacción Humano-Máquina, School of Architecture, Engineering, Science and Computing–STEAM, Universidad Europea de Madrid, C. Tajo, s/n, 28670 Madrid, Spain
Ana del Valle Corrales Paredes: Inteligencia Artificial e Interacción Humano-Máquina, School of Architecture, Engineering, Science and Computing–STEAM, Universidad Europea de Madrid, C. Tajo, s/n, 28670 Madrid, Spain
Sergio Bemposta Rosende: Inteligencia Artificial e Interacción Humano-Máquina, School of Architecture, Engineering, Science and Computing–STEAM, Universidad Europea de Madrid, C. Tajo, s/n, 28670 Madrid, Spain
José Manuel López López: Inteligencia Artificial e Interacción Humano-Máquina, School of Architecture, Engineering, Science and Computing–STEAM, Universidad Europea de Madrid, C. Tajo, s/n, 28670 Madrid, Spain
Enrique Puertas Sanz: Inteligencia Artificial e Interacción Humano-Máquina, School of Architecture, Engineering, Science and Computing–STEAM, Universidad Europea de Madrid, C. Tajo, s/n, 28670 Madrid, Spain
Asaf Levi Alfaroviz: La Empresa del Futuro, Faculty of Economic, Business and Communication Sciences, Universidad Europea de Madrid, C. Tajo, s/n, 28670 Madrid, Spain

JRFM, 2025, vol. 18, issue 11, 1-31

Abstract: Rising financial fraud impacts industries, economies, and consumers, creating a need for advanced technological solutions. Compliance frameworks help detect and prevent illicit activities like money laundering, market manipulation, etc. However, with the rise of cryptocurrencies and blockchain, traditional detection methods are ineffective. As a result, Artificial Intelligence (AI) has emerged as a vital tool for combating fraud in the cryptocurrency sector. This systematic review examines the integration of AI in compliance for cryptocurrency fraud detection between 2014 and 2025, analyzing its evolution, methodologies, and emerging trends. Using RStudio (Biblioshiny) and VOSviewer, 353 peer-reviewed studies from leading databases including SciSpace, Elicit, Google Scholar, ScienceDirect, Scopus, and Web of Science were analyzed following the PRISMA methodology. Key trends include the adoption of machine learning, deep learning, natural language processing, and generative AI technologies to improve efficiency and innovation in fraud detection. However, challenges persist, including limited transparency in AI models, regulatory fragmentation, and limited access to quality data, all of which hinder effective fraud detection. The long-term real-world effectiveness of AI tools remains underexplored. This review highlights the trajectory of AI in compliance, identifies areas for further research, and emphasizes bridging theory and practice to strengthen fraud detection in cryptocurrency transactions.

Keywords: fraud detection; compliance; artificial intelligence; machine learning; deep learning; natural language processing; generative AI; cryptocurrency fraud (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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