面向金融风险预测的时序图神经网络综述

金融风险预测在金融市场监管和金融投资中扮演重要角色, 近年来已成为人工智能和金融科技领域的热门研究主题. 由于金融事件的实体之间存在复杂的投资、供应等关系, 现有的金融风险预测研究常利用各种静态和动态的图结构来建模金融实体间的关系, 并通过卷积图神经网络等方法将相关的图结构信息嵌入金融实体的特征表示中, 使其能够同时表征金融风险相关的语义和结构信息. 然而, 以前的金融风险预测综述仅关注了基于静态图结构的研究, 这些研究忽视了金融事件中实体间关系会随时间动态变化的特性, 降低了风险预测结果的准确性. 随着时序图神经网络的发展, 越来越多的研究开始关注基于动态图结构的金融风险预测, 对这些研究进行系统、全面的回顾有助于学习者构建面向金融风险预测研究的完整认知. 根据从动态图中提取时序信息的不同途径, 首先综述3类不同的时序图神经网络模型. 然后, 根据不同的图学习任务, 分类介绍股价趋势风险预测, 贷款违约风险预测, 欺诈交易风险预测, 以及洗钱和逃税风险预测共4个领域的金融风险预测研究. 最后, 总结现有时序图神经网络模型在金融风险预测方面遇到的难题和挑战, 并展望未来研究的潜在方向.

Review on Temporal Graph Neural Networks for Financial Risk Prediction

Financial risk prediction plays an important role in financial market regulation and financial investment, and has become a research hotspot in artificial intelligence and financial technology in recent years. Due to the complex investment, supply and other relationships among financial event entities, existing research on financial risk prediction often employs various static and dynamic graph structures to model the relationship among financial entities. Meanwhile, convolutional graph neural networks and other methods are adopted to embed relevant graph structure information into the feature representation of financial entities, which enables the representation of both semantic and structural information related to financial risks. However, previous reviews of financial risk prediction only focus on studies based on static graph structures, but ignore the characteristics that the relationship among entities in financial events will change dynamically over time, which reduces the accuracy of risk prediction results. With the development of temporal graph neural networks, increasingly more studies have begun to pay attention to financial risk prediction based on dynamic graph structures, and a systematic and comprehensive review of these studies will help learners foster a complete understanding of financial risk prediction research. According to different methods to extract temporal information from dynamic graphs, this study first reviews three different neural network models for temporal graphs. Then, based on different graph learning tasks, it introduces the research on financial risk prediction in four areas, including stock price trend risk prediction, loan default risk prediction, fraud transaction risk prediction, and money laundering and tax evasion risk prediction. Finally, the difficulties and challenges facing the existing temporal graph neural network models in financial risk prediction are summarized, and potential directions for future research are prospected.