Fighting Financial Crimes with Graph Analytics: A Deep Dive
Graham Gannsle, Expero
Scott Heath, Expero
A surge in financial crime has increased the focus and scrutiny on the automated identification of fraud patterns and real-time alert identification. Join us as we dive deeply into maximizing machine learning processes by incorporating graph topology and algorithms. This session will include how to utilize the industry-standard FIBO ontology data model and incorporating 3rd party data like OpenCorporates and OFAC watch lists.
We'll cover the following key topics:
- Review practical uses and methods of key graph algorithms like Louvain, similarity page rank, and many others TigerGraph and ML.
- Detail the connection between graph algorithms and machine learning systems using topology feature extraction, embedding-based GraphML, deep learning, and other methods, to include with your existing platforms.
- Examine high-level system architectures for deploying real-time graph and machine learning systems that detect and prevent financial crimes.
Download the session slides here.
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