A microeconomic foundation for optimal money laundering policies

In this paper, we present a game-theoretic analysis of social networks in the money laundering process. In our model, criminals compete against each other in a crime market, but collaborate with other criminals and “dishonest” workers in the attempt to launder their crime proceeds via covert money laundering ties. Our first result shows that in the equilibrium money laundering network, a core group of criminals spreads its total crime proceeds over as many money launderers as available, giving rise to a core-periphery network where the size of the core group depends on the relative profitability of crime versus the outside option wage. We then study the optimal decision of a law enforcement agency that aims to minimize the total criminal activity in this society. We derive an optimal sharing rule that shows how much of a given crime-fighting budget the agency should optimally spend on anti-crime and anti-money laundering policies, respectively. This budget-sharing rule can be quantified empirically using readily available estimates for the expected crime proceeds, outside option wages, and fines in a society. Our predictions for four European countries (Sweden, the Netherlands, Poland, and Spain) show that the optimal budget share spent on money laundering controls should be about 35%.