---
title: "2021 Spring Graph + AI Summit: Fighting Financial Crimes with Graph Analytics: A Deep Dive"
description: 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.
image: https://info.tigergraph.com/hubfs/SPRING%202021%20GRAPH%20+%20AI%20(25).png
---

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# Graph + AI Summit Spring 2021 | Fighting Financial Crimes with Graph Analytics: A Deep Dive

### 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.](https://info.tigergraph.com/hubfs/04.21.21%20Graph%20+%20AI%20Summit/Leverage%20graph%20data%20to%20detect%20fraud%20in%20real-time.pdf)

![linkedin-icon](https://info.tigergraph.com/hs-fs/hubfs/Images/linkedin-icon.jpg?width=32&name=linkedin-icon.jpg) Connect with Scott on [LinkedIn](https://www.linkedin.com/in/scott-heath-4696779/) 

![linkedin-icon](https://info.tigergraph.com/hs-fs/hubfs/Images/linkedin-icon.jpg?width=32&name=linkedin-icon.jpg) Connect with Graham on [LinkedIn](https://www.linkedin.com/in/grahamganssle/) 

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