Graph Signal Processing and Graph Machine Learning

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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.