Price manipulation fraud detection by Intelligent Visual Fraud surveillance system

Abstract

For the first time, IVF (stands for Intelligent Visual Fraud) was introduced by Abbas Bagherian Kasgari (The first author of the current research) in 2016 as new solution for financial fraud detection. This study focuses on the development of an experimental model for evaluating the effectiveness of IVF in comparison with traditional alert-based surveillance systems in detecting price manipulation frauds. Fraud statistics of price manipulation in an emerging market is investigated. Regarding to previous researches, the case study approach was taken into action in the current research and the statistical population of the research was randomly selected among fraud cases during recent 5-year period ending to 2017. Binary regression and Logit model is used for evaluating the effectiveness of the manipulation detection approaches. The finding indicates that intelligent surveillance system can be served as suitable choice in detecting price manipulation in stock markets. What makes this research distinguished from previous researches is that this is the first time that the effectiveness of the new visual surveillance system is analyzed in stock market supervision. The finding opens a new window to future researches by upgrading the role of regulator from data worker to knowledge expert who plays as a designer and analyzer of suspected fraud patterns. The state-of-the-art idea initiates a new paradigm in financial and fraud prevention applications and realizes proactive surveillance dream.