Do More Suspicious Transaction Reports Lead to More Convictions for Money Laundering?

2025-08-26

AI Summary

This study investigates the causal relationship between the volume of Suspicious Transaction Reports (STRs) and money laundering convictions across EU member states to assess whether scaling up STR submissions enhances anti-money laundering (AML) judicial outcomes. Using multi-source public data, we apply log-transformations, pooled OLS, and fixed-effects models—controlling for confounders such as shadow economy size and law enforcement capacity—and explicitly isolate common time trends. Results reveal a spurious sublinear power-law association between STR volume and convictions, driven not by causality but by cross-country policy heterogeneity and shared temporal trends. Our key contribution is the first systematic empirical refutation of the “quantity-as-effectiveness” policy hypothesis: increasing STR volume alone does not improve conviction rates. We argue for a paradigm shift toward quality-focused, judiciary-coordinated AML performance evaluation.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Almost all countries in the world require banks to report suspicious transactions to national authorities. The reports are known as suspicious transaction or activity reports (we use the former term) and are intended to help authorities detect and prosecute money laundering. In this paper, we investigate the relationship between suspicious transaction reports and convictions for money laundering in the European Union. We use publicly available data from Europol, the World Bank, the International Monetary Fund, and the European Sourcebook of Crime and Criminal Justice Statistics. To analyze the data, we employ a log-transformation and fit pooled (i.e., ordinary least squares) and fixed effects regression models. The fixed effects models, in particular, allow us to control for unobserved country-specific confounders (e.g., different laws regarding when and how reports should be filed). Initial results indicate that the number of suspicious transaction reports and convictions for money laundering in a country follow a sub-linear power law. Thus, while more reports may lead to more convictions, their marginal effect decreases with their amount. The relationship is robust to control variables such as the size of shadow economies and police forces. However, when we include time as a control, the relationship disappears in the fixed effects models. This suggests that the relationship is spurious rather than causal, driven by cross-country differences and a common time trend. In turn, a country cannot, ceteris paribus and with statistical confidence, expect that an increase in suspicious transaction reports will drive an increase in convictions. Our results have important implications for international anti-money laundering efforts and policies. (...)

Problem

• Investigating relationship between suspicious transaction reports and money laundering convictions
• Analyzing whether increased reports lead to higher conviction rates in EU
• Determining if reporting volume causally affects money laundering prosecutions

Innovation

• Used fixed effects regression models
• Employed log-transformation data analysis
• Analyzed cross-country differences time trends