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

arXiv: https://arxiv.org/abs/2009.14588 · HTML: https://mlsanity.com/paper/2009.14588/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [Inductive Graph Unlearning](https://mlsanity.com/paper/2304.03093/) (2023)
- [Graph Neural Networks for Financial Fraud Detection: A Review](https://mlsanity.com/paper/2411.05815/) (2024)
- [On the combination of graph data for assessing thin-file borrowers' creditworthiness](https://mlsanity.com/paper/2111.13666/) (2021)
- [Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Users](https://mlsanity.com/paper/2204.11849/) (2022)
- [GNNBleed: Inference Attacks to Unveil Private Edges in Graphs with Realistic Access to GNN Models](https://mlsanity.com/paper/2311.16139/) (2023)

## Abstract (arXiv)

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