Utilizing LLM and Deep Learning Strategies to Amplify Algorithmic Proficiency in Detecting Complex Patterns of Insider Trading Fraud

In this study, we present a novel deep learning architecture for detecting insider trading activities in financial markets. By combining the strengths of both Falcon Language Learning Model (LLM) and Long Short-Term Memory (LSTM) networks, our proposed approach demonstrates exceptional performance in identifying unusual trading behavior. Specifically, LSTM is employed to examine historical stock prices for patterns and trends, while LLM lends its capabilities in extracting valuable knowledge from textual sources such as news articles and social media posts. To address the challenge of limited training data availability, transfer learning techniques are applied to facilitate easy updates and rapid detection. Through rigorous testing on actual instances of insider trading, we observe remarkable accuracy and minimal rates of false alarms. Furthermore, a methodical framework for evaluating the likelihood of each detected anomaly through calculated risk scores is presented, which enables more effective threat assessment and priority setting. Our findings have far-reaching implications for enhancing market transparency and mitigating risks in financial markets.