Towards near-real-time detection of insider trading behaviour through social networks

Abstract

Combining several theories concerning individual and group behaviour within social networks could help us spot unacceptable behaviour, such as insider trading. Social Network Analysis (SNA) helps us understand how people interact. Behaviour theory explains how individual humans behave and how we can differentiate usual and unusual behaviour. Co-ordination theory helps us understand the flow of control and group behaviour. Combining them can help us answer important questions, such as how actors in a social structure behave, co-ordinate and connect. But how can we spot abnormal behaviour by a group or an individual? Sumit Gupta and Liaquat Hossain suggest one way of achieving this in the fight against insider trading. The monitoring of capital frauds and malicious trading behaviours, and implementing changes to correct traders' and firms' behaviour, is increasingly seen as a priority in today's financial markets. Many governments and financial institutions are investing capital and resources to maintain the integrity of their markets and promote fair-trade practices. Of all the capital scams, insider trading is one of the hardest to detect and therefore the most difficult to prove in a court of law. © 2011 Elsevier Ltd.