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Wednesday, February 26 • 2:20pm - 3:10pm
Using Graphs To Analyze Customer Networks and Detect Fraud at Paysafe

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Paysafe provides simple and secure payment solutions to businesses of all sizes around the world, processing billions of payment dollars a year. This, combined with the focus of flawless customer experience and real-time money transfer, makes it a candidate for the “dark side” of the payments industry: fraudsters, money launderers, etc. With traditional data storage techniques such as relational technologies, it is almost impossible to see beyond individual accounts to the connections between them. In this session see how Paysafe implemented the property graph technologies in Oracle Spatial and Graph and Oracle Database, including its fast, built-in, in-memory graph analytics, to perform fast graph queries that identify patterns of fraud.A network is made of user accounts linked by shared characteristics – shared cards, devices, ips. Paysafe will share how analyzing and visualizing a heterogeneous graph of accounts and shared characteristics, can help identify customer networks. We will dive into network growth evaluation and see how it can help distinguish normal vs fraudulent network.

avatar for Dobroslav Hristov

Dobroslav Hristov

Senior Software Engineer, Paysafe Group
Currently I build java based services and their accompanying infrastructure as a team member of the Paysafe R&D department, and have participated for more than 13 years in the development of products for the Digital wallets division.
avatar for Marin Delchev

Marin Delchev

Machine Learning Engineer, Paysafe Group
Machine learning engineer with professional experience in the fields of natural language processing and networks analysis. I design, build and deploy machine learning solutions that save resources.

Wednesday February 26, 2020 2:20pm - 3:10pm PST
Bldg 23- Rm 1740 .