FiFrauD: Unsupervised Financial Fraud Detection in Dynamic Graph Streams
FiFrauD: Unsupervised Financial Fraud Detection in Dynamic Graph Streams is a scholarly work, published in 2024 in ''ACM Transactions on Knowledge Discovery from Data''. The main subjects of the publication include data mining, graph, artificial intelligence, fraud, STREAMS, market forecast, computer science, and blockchain. To solve this problem and alleviate the existing challenges, in this article, authors propose FiFrauD, which is an unsupervised, scalable approach that depicts the behavior of manipulators in a transaction stream.