Financial crime and conduct risk

Kimmo Soramäki and Samantha Cook

In this chapter, we present novel methods to improve fraud detection and anti-money laundering (AML) with network theory. We first present a network-based supervised learning model to detect anomalies of fraudulent large-value payments. We will then discuss techniques to automate the manual investigation of financial crime and “following the money”.


Suppose Alice makes a new payment to Bob. A moment later, Alice makes another new payment to Chuck. Can we know if either of the two transactions is suspicious?

This example has fictional characters commonly used in discussions about security protocols to illustrate the challenge of predicting anomalous payments. This task is important, as anomalous payments may be related to cybersecurity breaches, fraud, money laundering, terrorism financing, operational errors, exchange controls, illicit transactions, sanctions and politically exposed persons (PEPs). It is especially important during the Covid-19 crisis, as the pandemic has increased cyber risks. According to the FS-ISAC (2020), in the US, cyberattacks against financial institutions increased from 5,000 per week in February 2020 to more than 200,000 per week in

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