Responsible for human trafficking, terrorism, and fuelling narcotics – money launderers are a devious and dynamic bunch. They quickly learn the rules that companies set to detect suspicious transactions and adapt their strategy accordingly. To effectively counter crimes of such a nature, AML Transaction Monitoring solutions should therefore be equally dynamic and focus on these critical elements below.
Rules and thresholds
The application of rules and threshold criteria centred around customer segmentation are an essential part of the Transaction Monitoring process. However, in the complex world of global finance, where financial crime is becoming increasingly elaborate, rules-based systems alone are often not sufficient enough to detect illicit activity. Regulators have started putting pressure on companies to beef up their underlying systems and adopt more sophisticated analytics in their workflows.
Effectively rooting out complex money laundering schemes requires knowing far more about the connections between parties to transactions, as rules-based applications typically generate large amounts of false positives that need time consuming human investigation. Moreover, rules and outdated monitoring systems may not surface sophisticated new money laundering schemes designed to circumvent the rules already in place.
Adaptive machine learning
Fortunately, modern applications which utilise machine learning resolve some of these limitations when it comes to effective AML Transaction Monitoring. Machine learning feeds back what the system has learned and uses that knowledge to refine future analysis. It also ensures that the set thresholds are appropriate, consistently optimised, and rules are updated as the system learns – thus giving businesses the power to manage many more meaningful segments.
Network or link analysis
This element examines the key characteristics of parties to transactions and beneficial ownership, allowing businesses to better understand the connections between parties, which could help to identify the relationships between companies involved in money laundering schemes.
Behavioural analytics
Behavioural analytics enables businesses to monitor segmented customers for suspicious behaviour and also allows for segmentation based partly on the behaviour of similar businesses in a given location. If a company behaves in an atypical manner when compared to companies of the same nature, this could be an indication of illicit activity.
Each of these elements could work together to dramatically increase the power of AML Transaction Monitoring solutions, while avoiding the unnecessary flagging of thousands of transactions which need to be investigated manually. Together, these elements allow companies to spot information that has been manipulated and allow the monitoring of customers’ transactional activity over time to provide a more complete picture.
Combat financial crime with modern AML Transaction Monitoring Software
From profiling and segmentation to behaviour analysis and machine learning – AXON AML Transaction Monitoring offers a complete solution. If you’d like to know more about AXON and how it can help you remain compliant, contact Computime Software for more information, and remember to follow us on Facebook, LinkedIn, Twitter, and Instagram.
