Case Study

Accurate Detection & Optimized Routing

A key pillar of any AML program is to monitor transactions for suspicious activities. However, the rate of false-positives generated by the rigid rules-based system is high, as the latter cannot dynamically learn the complex behaviors behind money laundering.​
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IN BRIEF

KEY INFORMATION

COMPLIANCE

Reduce the number of false positives and false negatives preventing data base poisoning

ACCURATE DETECTION

With dynamically learning patterns in complex data

ADAPTABLE

Can be adapted to any architecture

Challenges

  • Reduce the high numbers of false positives induced by rule based systems​
  • Quickly Detect a potentially suspicious activity​
  • Increase the efficiency of the alert investigation process​

Solution

  • A detection and recognition of suspicious behavior​
  • An alert classification with heuristics applied to these alert classifications to determinethe “Next Best Action (NBA)​
  • An effective control framework​

Gains

  • Decrease in the number of false negative and false positive by about 70%​
  • Low cost of run for high efficiency​
  • Dynamic learning of new paterns​

Testimonial

“Effective and very easy to understand! Getting such a precision demonstrates a clear understanding of the underlying issue.”