Universal Graph Embeddings for Transaction data
Here at Mastercard, we have a transaction data where an account (card holder) transacts at a merchant, this data can be viewed as a Bipartite Graph of account and merchant nodes, where each edge represent a transaction between them. We have multiple models/tasks that provide predictive intelligence at an account level, e.g., predicting an account likely to experience a fraud, predicting an account likely to raise a chargeback, and predicting an account likely to default in near future. Goal of this work is to learn universal embeddings of account and merchants that can be leveraged to improve multiple downstream models.
View Full Project DescriptionDionne Aleman
Mastercard
Computer science
Professional, scientific and technical services
University of Toronto
Accelerate
