论文标题
EWS-GCN：用于交易银行数据的边缘重量共享卷积网络
EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data
论文作者
论文摘要
在本文中，我们讨论了如何将现代深度学习方法应用于银行客户的信用评分。我们表明，与使用目标客户的信息相比，基于他们之间的汇款之间有关客户之间联系的信息使我们能够显着提高信用评分质量。作为最终解决方案，我们开发了一种新的图形神经网络模型EWS-GCN，该模型通过注意机制结合了图卷积和复发性神经网络的思想。最终的模型允许对大规模数据进行强大的训练和有效处理。我们还证明，我们的模型优于最先进的图形神经网络，获得了出色的结果
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
