Internet Financial Fraud Detection Based on Graph Learning

Abstract

The rapid development of information technology such as the Internet of Things, Big Data, artificial intelligence, and blockchain has changed the transaction mode of the financial industry and greatly improved the convenience of financial transactions, but it has also brought about new hidden frauds, which have caused huge losses to the development of Internet and IoT finance. As the size of financial transaction data continues to grow, traditional machine-learning models are increasingly difficult to use for financial fraud detection. Some graph-learning methods have been widely used for Internet financial fraud detection, however, these methods ignore the stronger structural homogeneity and cannot aggregate features for two structurally similar but distant nodes. To address this problem, in this article, we propose a graph-learning algorithm TA-Struc2Vec for Internet financial fraud detection, which can learn topological features and transaction amount features in a financial transaction network graph and represent them as low-dimensional dense vectors, allowing intelligent and efficient classification and prediction by training classifier models. The proposed method can improve the efficiency of Internet financial fraud detection with better Precision, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> -score, and AUC.