A Random Forest Approach to Detect and Identify Unlawful Insider Trading

Abstract Detecting Unlawful Insider Trading (UIT) remains a critical yet challenging task. The sheer volume of transactions, often numbering in the thousands per minute, necessitates the development of sophisticated algorithms to augment manual review. While previous studies have demonstrated the potential of Machine Learning (ML) methods, such as Random Forest (RF), for UIT detection, this study significantly improves upon existing approaches. We expand the feature set from 25 previously used features to 110, incorporating 85 novel features that prove highly informative for UIT identification. Features were result of merging of multiple database for the first time to best of our knowledge. Furthermore, we substantially increase the training dataset size to 3,984 transactions. Consequently, our RF model achieves a UIT detection accuracy over 95 percent, a substantial improvement over previously reported results. Our key findings also highlight the significant influence of corporate governance factors on UIT and underscore the importance of data quality and feature engineering for optimal model performance. This research demonstrates the effectiveness of ML tools in automating UIT detection, thereby reducing reliance on manual analysis and enhancing efficiency.