Step 3 – Entity Resolution (Account Matching & Linking)
Welcome back to Matrix AML Academy. In case you missed our previous post, this is the 3rd part of an 8-part educational program on how to improve tour AML program and systems
In the previous chapter (#2), we focused on Data Quality Automation. But even if you did automate your data quality process you still need to make sure that your systems are able to detect and connect related accounts. Entity Resolution, Account Matching, and Linking are key activities in understanding customer risk, customer segmentation for screening and monitoring, and the thorough monitoring of customer activities (across multiple accounts) to detect and investigate transaction monitoring and fraud alerts.
One of the key challenges financial institutions face today is in depicting a complete view of customer relationships to better assess customer risk while monitoring customer transactions. Without a holistic 360-degree customer view, AML and fraud alert processing takes into account only limited account-based information of the customer. For example, account-based monitoring may not flag suspicious transactions amounting to $5,000/month in a certain customer account, yet customers may have (or control) 5, 10, or 50 other accounts that the surveillance models may not be linking together.
Data quality plays a key role in Entity Resolution and Account Linking. Good quality data is critical for high-confidence matching, as your firm connects the dots from data across the enterprise. Advancements in artificial intelligence and automation offer promising potential in uncovering relationships and expanding the traditional scope of data sources available to the compliance officer. Nevertheless, foundational data quality controls, such as validation, normalization, and standardization processes, are necessary to fully utilize these advanced capabilities.
Process Effectiveness Is Key
Firms are proactively putting in place efficient account matching and customer linking processes, and revamping their data quality management procedures to achieve a highly optimized transaction monitoring solution with proper segmentation of clients, thus allowing AML models to focus on unusual behaviors across entities with accurate grouping of transactions.
By enhancing data quality and completeness controls, financial institutions can attain a higher degree of confidence in the effectiveness of their transaction monitoring models along with improved alert consolidation capabilities and a reduction of false positives.
