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

Krishna Neupane, Igor Griva

2025

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.

Paper Citation

in Harvard Style

Neupane K. and Griva I. (2025). An Extreme Gradient Boosting (XGBoost) Trees Approach to Detect and Identify Unlawful Insider Trading (UIT) Transactions. In Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-758-0, SciTePress, pages 171-181. DOI: 10.5220/0013637500003967

in Bibtex Style

@conference{data25,
author={Krishna Neupane and Igor Griva},
title={An Extreme Gradient Boosting (XGBoost) Trees Approach to Detect and Identify Unlawful Insider Trading (UIT) Transactions},
booktitle={Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2025},
pages={171-181},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013637500003967},
isbn={978-989-758-758-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - An Extreme Gradient Boosting (XGBoost) Trees Approach to Detect and Identify Unlawful Insider Trading (UIT) Transactions
SN - 978-989-758-758-0
AU - Neupane K.
AU - Griva I.
PY - 2025
SP - 171
EP - 181
DO - 10.5220/0013637500003967
PB - SciTePress