A systematic literature review of illicit financial flows and money laundering: Current state of research and estimation methods

Aim/purpose – The goal of the paper is to examine the current state of research on illicit financial flows (IFF) and money laundering (ML) with a specific focus on detection and estimation methods. This study seeks to identify and evaluate the most promising approaches that can effectively counter IFF and ML in the context of economic stability, ensuring they remain adaptive to constantly evolving threats. Design/methodology/approach – The analysis was conducted on papers from Scopus and Web of Science, both recognized as leading academic databases. The application of screening analysis (1,249 papers) enabled the exclusion of articles not primarily focused on IFF and ML. In comparison, thematic synthesis (1,135 papers) facilitated the presentation of the current state of literature, highlighting main trends and categorizing articles by thematic dimensions. Textual narrative synthesis (234 papers) allows the identification of existing methods and the variables and proxies used in the literature to detect and estimate IFF and ML. Findings – The analysis reveals that a multidisciplinary approach to IFF and ML, integrating law, social sciences, and computer science, holds promise. The study emphasizes innovative methodologies, like machine learning, alongside gravity-based models. Specifically, 38% of the literature focuses on systematizing knowledge, while advanced techniques like machine learning (26%) and modified gravity-based models (3.33%) are increasingly influential. Legal and economic approaches provide a broad framework for understanding illicit activities and identifying emerging threats and trends among the methods used by criminals. Challenges remain in measuring and detecting IFF/ML due to the secretive nature of such flows. Research implications/limitations – This review summarizes IFF and ML detection/estimation techniques, including research up to 2024. Limitations include potential source selection bias (WOS/Scopus) and the exclusion of 81 studies due to access restrictions. Future research should refine source selection and improve access to critical literature. Originality/value/contribution – This study uniquely integrates thematic and textual narrative synthesis with bibliometric visualization to provide a comprehensive overview of research on IFF and ML. By reviewing existing detection and estimation tools in the context of emerging risks, the research offers valuable insights for enhancing the effectiveness of prevention strategies against these phenomena.