Graph Signal Processing and Graph Machine Learning

图信号处理和图机器学习

• 批准号: 2884089
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2884089
• 关键词: Graph; Signal; Processing; Machine; Learning

Despite the advancement in e-commerce continuously improving the economy and people's lives, individuals and business often become victims of fraudulent financial transactions with an estimated annual loss of £7 billion and £40 billion respectively. While it is important to develop a highly efficient regulation system to detect and prevent such fraudulent transactions, the complexity and high volumes of financial networks make it difficult to properly model this type of data. In this research, we aim to build effective AI models for financial regulation using machine learning techniques of Graph Neural Networks (GNNs), which model accounts and transactions in thefinancial network as nodes and links, then propagate information through the network. The key characteristic of this method is that when processing information of an individual node (i.e., financial account), the model will also gather information of its neighbours' behaviour (i.e., accounts with transactions) without searching over the entire network, making it an ideal approach to detect fraudulent clusters out of millions of financial transactions. In particular, our research will focus on Representation methods, which essentially work as a filter that can "screen out" the abundant irrelevant information in the financial network, and then use the remaining features as the "representations" to perform down-stream tasks. In addition to fraud detection, our research also works on other regulatory domains of financial networks, with the technical details covered in my research proposal. In credit risk management, we can model various financial entities (e.g., banks, companies, individuals and government) and their relationships (e.g., lending, borrowing and default) as a bank lending network. Then, with the entities' financial history, we can embed them into low-dimensional representations to analyse their behaviours and predict the default probability of loans, which helps lenders to make decision about new loans and monitor existing loans status at the same time. In stock market surveillance, we can treat the accounts and financial products as a bipartition graph with purchase/sell actions being the links among them. Then, by encode the graph into embeddings, we will be able to identify the clusters of entities that are highly correlated or have similar market behaviour, which can help regulators to detect anomalies in the market such as manipulation, insider trading, and other illegal activities.

尽管电子商务的进步不断极大地改善了经济和人们的生活，但个人和企业经常成为欺诈性金融交易的受害者，预计每年损失分别为 70 亿英镑和 400 亿英镑。制定有效的监管非常重要。由于金融网络的复杂性和大量数据使得对此类数据进行正确建模变得困难，因此我们的目标是使用图神经网络的机器学习技术构建有效的金融监管人工智能模型。 （GNN），对账户和交易进行建模该方法的关键特征是，在处理单个节点（即金融账户）的信息时，模型也会收集其邻居的行为信息（即具有交易的帐户），无需搜索整个网络，这使其成为从数百万笔金融交易中检测欺诈集群的理想方法，特别是，我们的研究将集中在表示方法上，该方法本质上充当可以“筛选”的过滤器。出“丰富的金融网络中不相关的信息，然后使用剩余的特征作为“表示”来执行下游任务除了欺诈检测之外，我们的研究还涉及金融网络的其他监管领域，技术细节在我的文章中有所介绍。在信用风险管理中，我们可以将各种金融实体（例如银行、公司、个人和政府）及其关系（例如贷款、借款和违约）建模为银行贷款网络。历史，我们可以将它们嵌入低维表示来分析他们的行为并预测贷款的违约概率，这有助于贷款人做出新贷款决策，同时监控现有贷款状况。在股市监控中，我们可以将账户和金融产品视为一个整体。然后，通过将图编码为嵌入，我们将能够识别高度相关或具有相似市场行为的实体集群，这可以帮助监管机构检测异常情况。这操纵市场、内幕交易等违法行为。