Affiliation:
1. Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Ave, Tehran, Iran
Abstract
Given a stream of financial transactions between traders in an e-market, how can we accurately detect fraudulent traders and suspicious behaviors in real time? Despite the efforts made in detecting these fraudsters, this field still faces serious challenges, including the ineffectiveness of existing methods for the complex and streaming environment of e-markets. As a result, it is still difficult to quickly and accurately detect suspected traders and behavior patterns in real-time transactions, and it is still considered an open problem. Therefore, to solve this problem and alleviate the existing challenges, in this paper, we propose FiFrauD, which is an unsupervised, scalable approach that depicts the behavior of manipulators in a transaction stream. In this approach, real-time transactions between traders are converted into a stream of graphs, and instead of using supervised and semi-supervised learning methods, fraudulent traders are detected precisely by exploiting density signals in graphs. Specifically, we reveal the traits of fraudulent traders in the market and propose a novel metric from this perspective, i.e., graph topology, time, and behavior. Then, we search for suspicious blocks by greedily optimizing the proposed metric. Theoretical analysis demonstrates upper bounds for FiFrauD's effectiveness in catching suspicious trades. Extensive experiments on five real-world datasets with both actual and synthetic labels demonstrate that FiFrauD achieves significant accuracy improvements compared to state-of-the-art fraud detection methods. Also, it can find various suspicious behavior patterns in a linear running time and provide interpretable results. Furthermore, FiFrauD is resistant to the camouflage tactics used by fraudulent traders.
Publisher
Association for Computing Machinery (ACM)
Reference67 articles.
1. Dalko V, Wang MH. High-frequency trading: Order-based innovation or manipulation? J Bank Regul. 2020 Dec 1;21(4):289–98.
2. Diaz D Theodoulidis B Sampaio P. Analysis of stock market manipulations using knowledge discovery techniques applied to intraday trade prices. Expert Systems with Applications. 2011 Sep 15;38(10):12757–71.
3. Alexander C, Cumming D. Corruption and Fraud in Financial Markets: Malpractice, Misconduct and Manipulation. John Wiley & Sons; 2020. 624 p.
4. Time series contextual anomaly detection for detecting market manipulation in stock market
5. Putniņš TJ. An Overview of Market Manipulation [Internet]. Rochester, NY: Social Science Research Network; 2018 Oct [cited 2019 Dec 6]. Report No.: ID 3398258. Available from: https://papers.ssrn.com/abstract=3398258