  Insider trading is one of the numerous white collar crimes that can
contribute to the instability of the economy. Traditionally, the detection of
illegal insider trades has been a human-driven process. In this paper, we
collect the insider tradings made available by the US Securities and Exchange
Commissions (SEC) through the EDGAR system, with the aim of initiating an
automated large-scale and data-driven approach to the problem of identifying
illegal insider tradings. The goal of the study is the identification of
interesting patterns, which can be indicators of potential anomalies. We use
the collected data to construct networks that capture the relationship between
trading behaviors of insiders. We explore different ways of building networks
from insider trading data, and argue for a need of a structure that is capable
of capturing higher order relationships among traders. Our results suggest the
discovery of interesting patterns.
