An extreme Gradient Boosting (XGBoost) Trees approach to Detect and Identify Unlawful Insider Trading (UIT) Transactions

TL;DR

This paper applies XGBoost, a machine learning technique, to detect and identify unlawful insider trading transactions with high accuracy, addressing the challenge of analyzing large, complex trading data.

Contribution

The study demonstrates the effectiveness of XGBoost in detecting unlawful insider trading and highlights its ability to rank feature importance for fraud detection.

Findings

XGBoost achieves 97% accuracy in identifying unlawful transactions.

The model provides feature importance rankings for fraud detection.

Machine learning enhances detection of complex illegal trading patterns.

Abstract

Corporate insiders have control of material non-public preferential information (MNPI). Occasionally, the insiders strategically bypass legal and regulatory safeguards to exploit MNPI in their execution of securities trading. Due to a large volume of transactions a detection of unlawful insider trading becomes an arduous task for humans to examine and identify underlying patterns from the insider's behavior. On the other hand, innovative machine learning architectures have shown promising results for analyzing large-scale and complex data with hidden patterns. One such popular technique is eXtreme Gradient Boosting (XGBoost), the state-of-the-arts supervised classifier. We, hence, resort to and apply XGBoost to alleviate challenges of identification and detection of unlawful activities. The results demonstrate that XGBoost can identify unlawful transactions with a high accuracy of 97 percent and can provide ranking of the features that play the most important role in detecting fraudulent activities.