Graph representation learning to prevent payment collusion fraud

Graph representation learning to prevent payment collusion fraud

Tuesday, June 19
2:00 PM - 2:40 PM
Grand Ballroom 220C

PayPal is at the forefront of applying large-scale graph processing and machine learning algorithms to keep fraudsters at bay. While deep learning algorithms have found enormous success in the application to unstructured data, their application to structured data, such as graphs, is in the nascent stage of research. One of the emerging research area is graph-based representation learning where features are learned automatically from network structure.

In this talk, I'll present how this emerging area can help to automatically learn features from a large scale payment network. Learned features are used to build machine learning models that are used to prevent complex fraudster activities where they collude with several other actors to commit fraud. I'll elaborate on specific challenges in applying this state-of-the-art machine learning algorithm to large scale payment network. Results from experiments conducted on a very large graph data set containing billions of edges and vertices will be presented.

SPEAKERS

Venkatesh Ramanathan
Data Scientist
PayPal
Venkatesh is a senior 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.