Leveraging Large Language Models and Deep Learning for Detecting Illegal Insider Trading

Illegal insider trading undermines the integrity of financial markets by exploiting non-public information for personal gain, posing risks to market fairness and investor trust. Addressing this issue is crucial to ensure transparency and maintain investor confidence. Estimates indicate that illegal insider trading occurs in about 20% of merger and acquisition events and 5% of quarterly earnings announcements. Our research introduces a deep learning-based approach that combines text classification, time series forecasting, anomaly detection and explainable AI to detect and predict potential illegal insider trading. By analyzing litigation related press releases and incorporating market and social media data, we develop a method to identify suspicious trading patterns. Our findings demonstrate that the proposed approach is promising in detecting potential instances of illegal insider trading, uncovering subtle patterns and anomalies. These insights offer financial regulators and institutions an enhanced surveillance mechanism, contributing to ongoing efforts to safeguard market fairness and reliability.