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

This paper presents a two-player Unity simulation game designed to teach anti-money laundering concepts, with particular attention to individuals who may be drawn into laundering schemes as money mules. Existing AML games have largely targeted corporate compliance teams, leaving individuals underserved. The game addresses this gap by placing one player in the role of a money launderer executing the placement-layering-integration model through shell companies, while the other acts as an AML specialist applying transaction monitoring, beneficial-ownership identification, and suspicious activity reporting aligned with FATF Recommendations 10 and 20. The design draws on game-based learning theory, with narrative framing and performance-linked endings intended to reinforce learning.

The principal empirical finding comes from an evaluation involving 50 students. Post-survey awareness scores of money laundering and AML concepts exceeded pre-survey scores, supporting the authors' conclusion that the game is effective for educating users about ML/AML principles and the regulatory institutions that enforce them. The evaluation also surfaced limitations: students judged the roughly 24-week gameplay, requiring at least one hour, too time-consuming, and requested a glossary of terms. These findings point to the need for shorter play sessions and supporting educational materials in future iterations.

The work's scope is primarily educational and technical rather than broadly comparative. Its theoretical grounding spans serious-games research and the psychology of emotion and memory, while its content draws on AML guidance from bodies such as the FATF and UNODC and on practitioner resources. Implementation relies on the Unity engine with Photon networking for multiplayer functionality. The evaluation is small in scale and short-term in nature, so its conclusions about learning gains should be understood as preliminary indications rather than definitive measures of long-term effectiveness.