FIs should first understand where the biggest opportunities for efficiency are in their AML programs
COMPLIANCE
FIs should first understand where the biggest opportunities for efficiency are in their AML programs
How to select a technology solution
Financial institutions ( FIs ) should first understand where the biggest opportunities for efficiency are in their AML programs . Once they assess what steps in procedures take the most time , they can begin to search for technology solutions that can automate them . Additional points to consider include :
• Ensure the technology provider understands both financial crime and technology . Working with a technology partner who does not understand how to use a tool against financial crime will require FIs to teach the partner the domain and help them figure out how to best apply said tool . A financial crime-focused technology provider will offer purpose-built solutions and know how to get them configured to match program specifications and quickly operational .
• Look for solutions that can complement existing core systems and processes , at least initially . Doing a wholesale replacement of a transaction monitoring system ( TMS ), case management system , know your customer system , or other core technology platform is expensive and time-consuming . To begin , select systems that can integrate with your core platforms and automate your current processes without replacing them . After developing a mature understanding of the capabilities and benefits of the new technology solution , an FI can consider replacing legacy tech , such as replacing rule-based TMS with a risk detection engine that generates fewer false positives while evaluating a broader set of risk-bearing characteristics and behaviors .
With respect to advanced AML technology solutions , FIs should look for the following capabilities :
• AI and machine learning : These technologies automate many of the steps an investigator conducts such as entity resolution and profiling , entity reputational analysis , transaction reviews and economic purpose derivation .
• Data clustering : This allows teams to efficiently divide data into discrete clusters . Cluster definitions can be predetermined by the user , or solutions can form clusters based on what the data says . For example , the technology can identify when a customer should shift from a low-risk cluster to a high-risk cluster . It can also call out when risk segmentation has shifted and automatically trigger high-risk customer reviews as part of enhanced due diligence efforts .
80 acamstoday . org