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

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. (...)

Figures (10)

• Figure 1: Illustration of our most basic pooled regression model (P1). $STR_{(c,t)}$ denotes the number of STRs filed in country $c$ in year $t$ per 100,000 capita. $CON_{(c,t+1:3)}$ denotes the average number of money laundering convictions per year in country $c$ up to three years after $t$ per 100,000 capita. We measure $t$ relative to 2006 (the start of our data collection). Thus, $\textit{t}=0$ corresponds to 2006, $\textit{t}=1$ corresponds to 2007, and so on.
• Figure 2: Observations of $CON_{(c,t)}$ per country $c$. We have 179 observations in total. There are five countries for which we have no observations: France, Greece, Latvia, Luxembourg, and Malta.
• Figure 3: Observations of $CON_{(c,t)}$ per year $t$. Note that $t$ is measured relative to 2006 (the first year of our data). We have 179 observations in total. The number of observations increases towards the end of our data collection period.
• Figure 4: Illustration of our most basic fixed effects regression model (FE1). $STR_{(c,t)}$ denotes the number of STRs filed in country $c$ in year $t$ per 100,000 capita. $CON_{(c,t+1:3)}$ denotes the average number of money laundering convictions per year in country $c$ up to three years after $t$ per 100,000 capita. We measure $t$ relative to 2006 (the start of our data collection). Thus, $\textit{t}=0$ corresponds to 2006, $\textit{t}=1$ corresponds to 2007, and so on.
• Figure 5: Q-Q plot of residuals for pooled regression model P1 using $CON_{(c,t+1:3)}$ as the dependent variable.