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How Banks Can Fight Back Against COVID-19 Emboldened Criminals

Written by Araliya Sammé, Head of Financial Crime at Featurespace (first published by SC Magazine)

Banks are already taking steps to ensure Covid-19 related threats are mitigated successfully. Now financial services could make some truly positive changes from the situation we find ourselves in.

One of the effects of dramatically changing work patterns and daily routine is that the behaviour of individuals from all walks of life is changing dramatically. This means that financial institutions relying on static rules-based risk management systems are likely to see huge numbers of false positives. This can cause AML investigators an unnecessarily large workload, or cause genuine transactions to be declined for Fraud teams, as thresholds are hit by unprecedented numbers of people with changed behaviours, for example: 

  • Unusual cross border transactions could increase as family members send financial aid to relatives in other countries or buy essential goods from abroad
  • Consumers may open new insurance policies, close policies before they normally would and generally act in ways that compliance professionals would deem suspicious
  • We even expect to see more buying and selling of gold and withdrawing of investment funds

This increase in false positives, combined with a dispersed risk management workforce, creates an ideal environment for criminals to hide among the unusual transaction behaviour. They can work safer in the knowledge that the rest of the population is behaving as the criminals are, albeit with more honest intentions.

Criminals moving fast 

We have already seen an uptick in fraudulent behaviours like scams taking advantage of the fear that many are feeling about the pandemic. We know that where there is fraud, money laundering is likely to follow. There are already predictions of money muling rings gaining many new potential mules, with job losses creating a populace driven to make money in unconventional ways. We have seen reports of Coronavirus 'donations' being used as the premise to get unsuspecting employees to move Bitcoin currency around, adding new mule threats to warn consumers about. 

This leaves financial institutions under increasing pressure as they deal with rising alert levels and existing backlogs, while also managing unusual remote working environments. 

Meeting the challenge

Financial institutions can meet this challenge by moving quickly to ensure that staff can work effectively from home by prioritising providing the right data to their investigators. There should be a renewed focus on banks keeping up a high standard of transaction monitoring even if working conditions might be difficult. Ensuring investigators have access to necessary customer records is a simple way of making their work easier during this time. 

With the potential increase in false positives, prioritisation is more important than ever. Machine learning-based adaptive behavioural analytics can do the heavy lifting here, enabling investigators to focus on the highest priority alerts by eliminating a high percentage of false positives. 

It is more important than ever to create good quality Suspicious Activity Reports (SARs). Financial institutions would benefit hugely from understanding individual behaviours to reduce false positives and prioritise alerts effectively without relying on manpower, using adaptive rules and advanced machine learning for more effective transaction monitoring. 

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Aite Fraud and AML Machine Learning Platform Evaluation

Aite Group, a global research and advisory firm, has recognized Featurespace™ as Best-in-Class in its 2019 report on fraud and AML machine learning platform vendors.

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About Featurespace – www.featurespace.com

Headquartered in the U.S. and U.K. and with offices in Atlanta, Cambridge and London, Featurespace™ is the world-leader in fraud prevention and creator of the ARIC™ platform, a real-time AI machine learning software that risk scores events in more than 180 countries.

The ARIC platform combines adaptive behavioural analytics and anomaly detection to automatically identify risk and catch new attacks as they happen. The increased accuracy of understanding behavior strikes the balance between improving fraud and risk detection and operational efficiencies, while also reducing the number of genuine transactions that would be incorrectly declined by as much as 70 percent.

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