CaPE: Category Preserving Embeddings for Similarity-Search in Financial Graphs

Abstract

Similarity-search is an important problem to solve for the payment industry having user-merchant interaction data. It finds out merchants similar to a given merchant and solves various tasks like peer-set generation, recommendation, community detection, and anomaly detection. Recent works have shown that by leveraging interaction data, Graph Neural Networks (GNNs) can be used to generate node embeddings for entities like a merchant, which can be further used for such similarity-search tasks. However, most of the real-world financial data come with high cardinality categorical features such as city, industry, super-industries, etc. which are fed to the GNNs in a one-hot encoded manner. Current GNN algorithms are not designed to work for such sparse features which makes it difficult for them to learn these sparse features preserving embeddings. In this work, we propose CaPE, a Category Preserving Embedding generation method which preserves the high cardinality feature information in the embeddings. We have designed CaPE to preserve other important numerical feature information as well. We compare CaPE with the latest GNN algorithms for embedding generation methods to showcase its superiority in peer set generation tasks on real-world datasets, both external as well as internal (synthetically generated). We also compared our method for a downstream task like link prediction.