Abstract
Today financial institutions have been investing billions of US dollars to detect money laundering. When financial institutions are found to have their customers conduct money laundering through them, they are subjected to large penalties. Moreover, their reputation suffers greatly through public exposure. In response, financial institutions have been exploring opportunities to use graph machine learning algorithms. This paper describes one of those algorithms called Anti-TrustRank and demonstrates how it can be used to identify money launderers. In contrast to many other algorithms, Anti-TrustRank calls for selecting a very small set of customers to be confirmed by human experts (e.g., compliance officers or analysts) as money launderers. Once this set has been identified, Anti-TrustRank seeks out customers linked (either directly or indirectly) to those money launderers.
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