A Simulation Game for Anti-money Laundering (AML) Using Unity

Abstract

With increasing demand of anti-money laundering (AML) regulation in Fintech, AML is one of the key factors in FinTech and its regulatory technologies (RegTech). Presently, as research and education on AML focus on financial institutions and authority, the individual is vulnerable to money laundering (ML) by being money mules with lack of awareness. Therefore, this paper illustrates the design of a 2-player simulation game for AML, which integrates the game-based learning model with plots including introduction stories, player actions and ending stories. In the game, a player role-plays either a money launderer or AML specialist. Within 6 in-game months, the former needs to perform ML with a target goal while the latter needs to identify the former’s actions and restrict him to achieve his goal.
 For actions of the money launderer, this paper integrates the criminal order with the PLI model (placement, layering and integration) to simulate the full ML circle. The criminal order provides return to the attacker if he completes it within the time limit. Each layer in the PLI model is expanded with middle processes for the methodology. The attacker uses shell companies to hide his identity and support each transaction for ML with apparently legitimate reasons.
 For actions of the AML specialist, this paper integrates the AML transaction monitoring with the Financial Action Task Force (FATF)’s Forty Recommendation. The defender needs to perform AML transaction monitoring with identifying suspicious financial activities based on money flow. Then, he needs to identify the actual beneficial owner of suspected companies with their share distributions. Both money flows and share distributions are visualized in data charts. Later, the defender shall report suspicious companies to the Financial Intelligence Unit (FIU), which will return the investigation result at the beginning of the next in-game month.