Investigating and mitigating financial fraud is a critical aim for global organizations. However, you need the right tools – like advanced graph algorithms – to stop fraudsters in their tracks.
Let’s (un)Do Crimes: Using Graph Analytics for PaySim Data Fraud Detection with Neo4j Field Engineer Dave Voutila explores tactics for detecting financial fraud using graph technology.
In this session, Dave covers:
- Generating realistic, synthetic financial transactions with PaySim
- Using the Graph Data Science Library to analyze data
- Providing a live graph algorithms demo of detecting risky or fraudulent behavior in PaySim
Dave is a transplant to the state of Vermont, having grown up and spent most of his life in Massachusetts. After receiving his degree in mathematics, he worked in a variety of sales engineering roles at software startups and spent some time as an independent consultant. Prior to joining the Neo4j team, Dave was VP of Solutions for a SaaS startup where he was a full-stack engineer as well as the lead cloud systems engineer.
When not working, Dave's an okay ultramarathoner, likes skiing downhill and Nordic, and enjoys hiking the Green Mountains with his wife and their golden retriever. Not to be left out, his cat maintains Chief Feline Officer status in his household.
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