Combat Money Laundering with Graph Analytics, Machine Learning, and Financial Crimes Toolkit
Recorded February 10, 2021
The U.S. Government has implemented regulations requiring financial institutions to collect and report sender and receiver details for a broad range of transactions. Legacy rules-based systems have limited the efficacy of anti-money laundering (AML) detection, investigation, and reporting. As a result, the financial institutions are at risk of huge fines for failure to comply with these laws.
This webinar’s theme is a ‘cooking show’ that blends TigerGraph’s graph database and TigerGraph Financial Crimes Toolkit to deliver the next-generation solution for AML detection, investigation, and reporting. TigerGraph based solution augments legacy AML systems, finding alerts missed by current methods and offering 10% or higher efficiency in processing the AML alerts.
Join us as we cover the following key topics:
- Key business challenges addressed by TigerGraph’s AML Solution
- TigerGraph DB for AML - Review practical uses and methods of money laundering activity identification, complex dependency, and case management with ‘humans-in-the-loop’ for higher accuracy.
- TigerGraph UI Toolkit for AML - Design and use the AML specific features in the TigerGraph database. This session will show how to find non-obvious relationships using patterns powered by graph algorithms, alert matching, and case management features. These powerful visualizations lower the false positives and increase accuracy.
- Putting together the next-generation AML solution - Wrap up the session with the whole picture of how the TigerGraph platform solves the most complex AML challenges in a new way.
Scott Heath, Expero
Michael Shaler, TigerGraph
Steven Fuller, TigerGraph