Analysis of the main techniques and tools to combat money laundering: a review of the literature

Abstract

[Abstract] The purpose of this paper is to systematically review and evaluate recent anti-money laundering (AML) research, focusing on methodological shifts toward machine learning and network analysis, and identify key challenges and future directions for effective and ethical AML. This is a systematic review that follows Preferred Reporting Items for Systematic Reviews and PRISMA guidelines. An analysis of 45 studies (2017–2024) was conducted via Google Scholar using structured content analysis with a bi-dimensional framework (methodology and contextual applicability) AML research shows a paradigm shift from statistics to machine learning and network analysis. Mixed methods are increasingly important. Key challenges include cryptocurrencies, balancing detection with privacy and model interpretability/scalability. The literature shows significant variation in methods and results across operational contexts, but few studies offer direct comparisons of their relative effectiveness. Network analysis effectiveness depends on regulatory context and data sharing. The reviewed studies reveal ongoing discussion and varied approaches regarding model complexity versus practical applicability in diverse settings. Similarly, a debate on the factors influencing network analysis effectiveness emerges, frequently pointing to the critical roles of regulatory frameworks and data-sharing capabilities, though without a unified consensus on optimal implementation across all contexts his study reveals the need for research into adaptable models, context-specific solutions, privacy-preserving analytics and the interplay between AML evolution and criminal adaptation.