As regulators put more spotlight on the importance of Transaction Monitoring relative to Anti-Money Laundering (AML), companies face the challenge of getting their existing systems in order. They must beef up their underlying applications and take a more robust approach to Transaction Monitoring and anti-money laundering software in order to maintain their compliance.
Due to a number of factors, customers tend to transact differently from one another which is where segmentation comes into play. Based on various threshold scenarios, analysing customer segments allows for more focused AML Transaction Monitoring, thus reducing the amount of false positives being triggered. However, segments and thresholds can only do so much in the midst of the ever increasing and evolving regulatory requirements.
The quality of data used
One of the most significant areas concerning a stronger approach towards Transaction Monitoring is the quality of data collected by the business. They should require accurate and up-to-date information about the customer, accounts, products, financial institutions, and jurisdictions involved. Companies must make sure that their systems are updated or re-designed to capture the data associated with these categories.
Rules and customer segmentation
Money laundering has become more organised and elaborate over time, making every risk attached to each transaction a demanding, multi-dimensional issue. This is especially true of outdated anti-money laundering software and monitoring systems, which rely on a combination of segmentation and threshold criteria to analyse multiple layers of data. Newer systems, on the other hand, use volume and value as a basis on which to set high-level thresholds. Adversely, the more customer segments you have, the higher the number of thresholds you’ll need to maintain within the rules. Likewise, this method makes no clear distinction between risk profiles of different clients, no matter the size or risk rating.
The best approach to AML Transaction Monitoring
Although the above factors are important, they’re not enough. Money launderers nowadays can easily avoid suspicion by splitting a transaction into several amounts below the trigger threshold. In order for a Transaction Monitoring approach to be truly effective, anti-money laundering software must analyse the individuals behind any unusual behaviour, the related transactions, and interconnected relationships. It must also incorporate the following four elements:
- A rules-based set of thresholds and segmentation
- An examination of relationships between parties (as well as third parties) through network analysis
- Recognising abnormal patterns of transactions through behaviour analytics and dynamic peer groupings to be able to compare customers
- Continually improving the system by utlising feedback and machine learning effectively.
AXON AML Transaction Monitoring automatically looks for suspicious transactional activity and analyses it against a customer’s activity history and profile to provide companies with a real-time view into any potential AML issue. Contact Computime Software today for more information, and remember to follow us on Facebook, LinkedIn, Twitter, and Instagram.