Amberoon Lucre

Preventing money laundering in today’s global economy is an extremely important component of banking operations. The inability to prevent this activity exposes banks to unknown risks. In recent times, many banks have had to pay large fines (hundreds of millions of dollars) because of their inability to uncover Anti-Money Laundering (AML) risks.

Nine out of ten alerts generated by a typical AML (anti-money laundering) monitoring system are false positives. All alerts need to be scrutinized to find those that require filing of a SAR (Suspicious Activity Report). Reviewing large numbers of alerts to find a few bad actors increases operational expense as well as risk. Meanwhile, money-launderers continue to find newer ways to disguise illicit activities as legitimate transactions, making them harder to detect. This makes them a moving target for AML investigators. 

Amberoon Lucre uses a risk-based approach to AML which works in conjunction with legacy AML systems to implement a System of Insight for banking operations. Lucre uses data from transactions, external sources and internally developed proprietary algorithms that enable forensic human inquiry to identify bad actors and reduce risk

Risk-based Approach

A risk-based approach to AML can improve monitoring and compliance while reducing costs. Lucre enables a risk-based approach by focusing a bank's efforts on a small set of transactions with the highest probability of AML risk.

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Cognitive Analytics

Technology developments in big data, machine learning and predictive analytics provide the basis for mitigating AML risk. These technologies can be used to identify patterns of bad behavior that are very hard for a human being to detect.

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Reduce Compliance Exposure

Banks can use Lucre to reduce compliance exposure by improving existing banking processes and by creating new approaches to managing existing AML processes including setting up effective internal controls and alert systems.

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