AML alerts management

Client Needs & Objectives

Use of a standard framework for AML alert management with usual filtering tools (Norkom, Actimize etc.) based on static rules and leading to many operational constraints:

  • High number of false positive hits all to be manually released
  • Low suspicious activity report conversion rate
  • Highly labour intensive analysis tasks

Our approach

Use Big Data and Machine Learning capabilities to detect more precisely client money laundering suspicious activities and reduce volume of false positives

Client Benefits & Main Results

  • Improved operational efficiency on Business As Usual tasks by reducing time spent on manual analyses and release of obvious false positive hits

  • Enhanced and secured AML alert management framework robustness by replacing static rule-based scenarios with patterns and behavioral client transactions detection algorithm

  • Managed more efficiently and timely alerts backlog

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