Assessing the value of objective suspicious transactions in Dutch anti-money laundering investigations: a network approach

Abstract The increasing volume of suspicious transaction reports (STRs), covering transactions that may be generated by criminal activity, presents both opportunities and challenges for anti-money laundering (AML) investigations. Objective STRs, automatically triggered based on predefined criteria, are generally de-prioritized by investigators compared to subjective STRs filed on expert judgment. In this study, we analyze the investigative value of objective STRs using five years of data from the Dutch Anti-Money Laundering Centre, part of the Dutch Investigation Service for Financial and Tax Crime (FIOD), one of the national agencies involved in identifying and investigating money laundering. We apply a network science framework to analyze how objective STRs contribute to (i) expanding the number of unique entities linked to investigated cases, (ii) connecting previously disconnected dossiers, and (iii) improving predictive accuracy for identifying entities under investigation. Although objective STRs represent a small fraction of reports, they increase network coverage and connectivity, adding new entities and dossiers, including cases under investigation. However, including objective transaction data when computing centrality-based predictors does not improve the accuracy of identifying entities under active investigation. These findings suggest that, while objective reports help flag potential cases, more descriptive reporting is needed to support investigations.