Improving client risk classification with machine learning to increase anti-money laundering detection efficiency
Endre Jo Reite,
Johan Karlsen,
Elias Grefstad Westgaard
Abstract:Purpose
This study aims to describe and empirically explore a new method for bank anti-money laundering (AML) systems using machine learning models. Current automated money laundering detection systems are notorious for flagging many false positives, causing bank employees to spend unnecessary time manually checking transactions that do not constitute money laundering. Decreasing the number of false positives can free up resources for investigating money laundering.
Design/methodology/approach
This study use… Show more
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