Review of artificial intelligence-based applications for money laundering detection

• This study systematically reviews AI-based solutions for money laundering detection over nine years. • We analyze money laundering activities in terms of structure, impact, detectability, and regulation to inform detection strategy selection. • The study examines AI techniques (e.g., supervised/unsupervised learning, deep learning) and contemporary solutions (e.g., social network analysis) to assess their strengths, weaknesses, and popularity. • A smart literature review is conducted using the PRISMA framework and LDA topic modeling to identify the most common topics. Since studies of pattern recognition for detecting money laundering have overflowed with various outcomes, effective applications of artificial intelligence (AI) for delivering précised outcomes are still emerging. In this paper, we evaluate AI-based approaches for their performance measure (e.g., accuracy), data requirement, processing speed, and cost-effectiveness in detecting money laundering activities, find related gaps, and suggest possible courses of action. Adopting a smart literature review analysis, including PRISMA and a topic modeling technique, this study examines published peer-reviewed and conference articles from 2015 to June 2023. The study identifies dominant topics in the period, concluding that AI-based solutions have increasingly been deployed in detecting money laundering, though they face various challenges in application. It also emphasizes that AI solutions are required to be evaluated to measure their performance before applying to large-scale problem-solving.