Legacy, rule-driven monitoring systems are plagued by high false positives and low catch rates. Banks add new rules to address each problem as they find them. Over the course of 20 years, this strategy has led to many broad rules that are intended to cover all use cases. In reality, these rules catch all kinds of legitimate activity. Further, current offerings do not have the capacity to identify more complex patterns of activity within large databases. Our application of machine learning to this problem is much more flexible and is able to account for many more variables simultaneously. Our algorithms learn from every investigation that your analysts conduct to constantly improve and stay ahead of the criminals. We also score and rank our parameters by analyzing thousands of cases in order to prioritize hits and give them greater risk weighting. All of this culminates in fewer false positives and higher catch rates, which equates to less wasted time and more quality SARs filed.
Current systems produce up to 99% false positives and still miss as much as 60% of bad activity.
Beam streamlines the process of investigating and reporting suspicious activity by integrating our powerful tools with a modern, intuitive workflow. From automated gathering and presentation of data to simplified filing of regulatory reports, Beam helps your analysts seamlessly moves a case to filing.
Better tools deliver better results. Better results in compliance mean more illegal activity caught, less time wasted, and the avoidance of large fines. We keep you updated with the latest models and data sources so that you always have the tools to stay compliant and reduce the chance of penalties.
Legacy rule and scenario based systems are fine for detecting static money laundering vectors. But criminals don’t sit still, and financial institutions can’t either. When behavioral patterns change, legacy systems do not adapt. Over time, they lose their precision. The only solution is to add more rules, which over time, generates huge numbers of false positives and even worse, makes false negatives more likely.
Our domain-specific models adapt to changing behavior and perform better in a wider range of scenarios with superior accuracy. By pursuing fewer false positives deprioritized by Beam’s technology, you’ll be able to detect more genuinely suspicious activity.