Searching for Smurfs: Testing if Money Launderers Know Alert Thresholds

• In Event: EUROC 6 – Corruption and Public Institutions

Abstract

To combat money laundering, banks raise and review alerts on transactions that exceed confidential thresholds. But these secret thresholds can be leaked to criminals, through e.g. corruption, an insider job, or extortion. This paper presents a data-driven approach to detect smurfing, i.e., money launderers seeking to evade detection by breaking up large transactions into amounts under the secret threshold. The approach utilizes the notion of a counterfactual distribution and relies on two assumptions: (i) smurfing is unfeasible for the very largest financial transactions and (ii) money launderers have incentives to make smurfed transactions just under the threshold. Simulations suggest that the approach can detect smurfing when as little as 0.1-0.5% of all bank transactions are subject to smurfing. An application to real data from a systemically important Danish bank finds no evidence of smurfing and, thus, no evidence of leaked confidential thresholds. An implementation of our approach will be available online, providing a free and easy-to-use tool for banks to check whether their secret thresholds were leaked.

Authors

• Rasmus Ingemann Tuffveson Jensen, Aarhus University

• Joras Ferwerda, Utrecht University School of Economics

• Christian Remi Wewer, Aarhus University