Using Network Analysis to Combat Money Laundering & Fraud

Introduction

 

Money laundering and fraud cost financial institutions around the world billions annually in losses and regulatory fines. Between 2008 and 2018, nearly US$27B was paid out for AML/KYC/Sanctions fines with the average fine being US$90M.[1]

 

Currently, most detection schemes rely heavily on business rules and supervised machine learning models, as well as large teams of analysts to deal with the fallout from high false positive rates – rates that are essentially inevitable due to class imbalance and garbled labeling of historical data.

 

Methodologies used by money launderers, fraudsters, and other bad actors can often also be reframed as network analysis problems which provide potentially much better detection rates, resulting in reduced costs and limited losses. Request your free copy of Using Network Analysis to Combat Money Laundering & Fraud to see a few widely-used criminal methods, along with specific examples and ideas as to how network analysis can be applied for detection.

[1] “Global Financial Institutions Fined $26 Billion for AML, Sanctions & KYC Non-Compliance.” Fenergo, 9/26/2018, www.fenergo.com/press-releases/global-financial-institutions-fined-$26-billion-for-aml-kyc.html.

Money Laundering Methodologies

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