Temporal Graph Learning for Financial World: Algorithms, Scalability, Explainability & Fairness

Abstract

The most intuitive way to model a transaction in the financial world is through a Graph. Every transaction can be considered as an edge between two vertices, one of which is the paying party and another is the receiving party. Properties of these nodes and edges directly map to business problems in the financial world. The problem of detecting a fraudulent transaction can be considered as a property of the edge. The problem of money laundering can be considered as a path-detection in the Graph. The problem of a merchant going delinquent can be considered as the property of a node. While there are many such examples, the above help in realising the direct mapping of Graph properties with the financial problems in the real-world. This tutorial is based on the potential of using Graph Neural Network based Learning for solving business problems in the financial world.