EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data

TL;DR

This paper introduces EWS-GCN, a novel graph neural network model that leverages transaction-based client connections to significantly enhance credit scoring accuracy in banking.

Contribution

The paper presents a new graph neural network architecture combining graph convolutional and recurrent neural networks with attention, tailored for large-scale transactional banking data.

Findings

EWS-GCN outperforms existing graph neural networks in credit scoring tasks.

Incorporating transaction connections improves credit scoring accuracy.

The model demonstrates robust training and efficient processing of large datasets.

Abstract

In this paper, we discuss how modern deep learning approaches can be applied to the credit scoring of bank clients. We show that information about connections between clients based on money transfers between them allows us to significantly improve the quality of credit scoring compared to the approaches using information about the target client solely. As a final solution, we develop a new graph neural network model EWS-GCN that combines ideas of graph convolutional and recurrent neural networks via attention mechanism. The resulting model allows for robust training and efficient processing of large-scale data. We also demonstrate that our model outperforms the state-of-the-art graph neural networks achieving excellent results