Improving AML Detection and Investigation using ML and Graph Databases

In today’s fast-paced and increasingly digital world, effectively combating financial crime and achieving regulatory compliance demands smarter, faster, and more dynamic tools and solutions. Legacy rules-based systems have limited the efficacy of anti-money laundering (AML) detection and investigation. Financial organizations lack holistic and connected views of parties, accounts, and transactions due to messy and fragmented data, inabilities in harnessing that data, and high numbers of false-positive alerts. Hidden and unknown financial crime risks were often left undetected, and AML investigations became overly complicated, time-consuming, and inefficient. Graph algorithms combined with machine learning offer a more modern, intelligent, and streamlined approach in fighting, monitoring, and investigating illicit activity. These technologies enable a path to connected intelligence and elevated analytics. Financial organizations can improve their understanding of financial risks and optimize AML detection with higher quality alerts that find suspicious activity missed by other solutions. Investigators can visualize the network of parties, accounts, and transactions to make more informed decisions. The solution has delivered over 10% improvement in AML investigation efficiency.

  • Discover how graph technology can dynamically connect parties, accounts, and transactions across disparate data sources, elevating financial crime detection, investigation, and intelligence.
  • Learn how connections, patterns, and anomalies can drive productive investigations, inform prioritization, hibernation, escalation of work and alert activity, and lead to better decision-making.
  • Uncover techniques to integrate graph algorithms for machine learning into current financial crime strategies, processes, and systems.

David Ronald, Product Marketing Director, TigerGraph
Steven Fuller, Senior Solutions Engineer, TigerGraph

Download the presentation here.