Using a Model of Fraudulent Trader for Fraud Detection

The technological revolution brought by the internet, high performance computing, and artificial intelligence has fundamentally changed and continues to alter the landscape of finance. These innovations, if used with a malicious intent, can seriously destabilize the financial market. For this reason, counter-measures in the form of new detection methods are needed. In this study, we propose a novel detection framework that uses a model of fraudulent behavior to detect fraud from observed data. A similarity measure is defined to decide if the recorded actions of a monitored trader are matching actions of the fraudulent agent. We illustrate the framework on a simple form of manipulative trading in a simulation environment of a market consisting of two exchanges. This demonstrative case study is inspired by a price manipulation scheme that occurred on the Bitcoin market in 2017/18, where such simple forms of manipulation were observed. Simulation results outline vulnerabilities in markets, where uneven distribution of liquidity is present, as this can be exploited by pump-and-dump scheme.