Graph Representation Learning to Prevent Payment Collusion Fraud – Databricks

Graph Representation Learning to Prevent Payment Collusion Fraud

PayPal is at the forefront of applying large scale graph processing and machine learning algorithms to keep fraudsters at bay. In this talk, I’ll present how advanced graph processing and machine learning algorithms are applied at PayPal for fraud prevention.

I’ll give an in-depth overview of emerging area of graph based machine learning. I’ll elaborate on specific challenges in applying large scale graph processing & machine technique to payment fraud prevention. I’ll present results from experiments conducted on a very large graph data set containing billions of edges and vertices.

Session hashtag: #AI6SAIS



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About Venkatesh Ramanathan

Venkatesh is a Data Scientist at PayPal where he is working on building state-of-the-art tools for payment fraud detection. He has over 20+ years experience in designing, developing and leading teams to build scalable server side software. In addition to being an expert in big-data technologies, Venkatesh holds a Ph.D. degree in Computer Science with specialization in Machine Learning and Natural Language Processing (NLP) and had worked on various problems in the areas of Anti-Spam, Phishing Detection, and Face Recognition.