Improving AML Detection and Investigation with Machine Learning and Graph Algorithms
About this webinarIn 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 lack holistic and connected views of parties, accounts, and transactions due to fragmented data, inabilities to harness that data, and high false-positive rates. Graph algorithms combined with machine learning offer a more modern, intelligent, and streamlined approach in fighting, monitoring, and investigating illicit activity. Financial organizations can improve their understanding of risks and optimize AML performance 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. Learning Objectives:
- Discovering how graph technology can dynamically connect parties, accounts, and transactions across disparate data sources, elevating financial crime detection, investigation, and intelligence.
- Learning how connections, patterns, and anomalies can drive productive investigations, inform prioritization, hibernation and escalation of work and alert activity and enhance decision-making.
- Uncovering techniques to integrate graph algorithms for machine learning into current anti-financial crime strategies, processes, and systems.