Improving AML Detection and Investigation with Machine Learning and Graph Algorithms

Recorded
En ligne
1 ACAMS Credit
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Sponsor

TigerGraph

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.

Who Should Attend?

BSA / AML compliance officers, AML analysts and investigators, Financial crime practitioners, Risk management officers, Law enforcement, Data scientists, Consultants and system integrators, IT specialists

Region / Industry

Global, Financial services

Level

Intermediate