I present a new perspective on 'money laundering,' understanding it from a risk perspective using confirmatory factor analysis (CFA). I initially discuss the models studied so far in the money laundering and anti-money laundering literature, pointing out their shortcomings. I then set up my CFA model to identify the hidden factors of money laundering risk using observed variables across 203 countries. I compare my model with a competing data configuration proposed by the Basel Institute on Governance. I present a comprehensive application of CFA to understand how to combat money laundering risk and touch on the role of structural equation modelling in anti-money laundering policy-making. Using this method, I illustrate the hidden dimensions of money laundering risk. My findings will be useful for anti-money laundering policy experts around the world.
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Thesis advisor: Hira, Anil
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