Comprehensive Approaches to Risk Management and Fraud Detection in Algorithmic Trading: Analyzing the Efficacy of Predictive Models and Real-Time Monitoring Systems

Abstract

Algorithmic trading has transformed financial markets by enabling faster and more efficient trade execution. However, this shift has introduced significant risks, including market volatility and increased susceptibility to fraud. This paper explores comprehensive approaches to risk management and fraud detection within algorithmic trading, focusing on the efficacy of predictive models and real-time monitoring systems. Predictive models, enhanced by machine learning and AI, allow traders to forecast risks and prevent losses by analyzing historical and real-time market data. Real-time monitoring systems, on the other hand, detect fraudulent activities by identifying abnormal trading patterns. Despite their potential, both approaches face challenges related to accuracy, scalability, and regulatory compliance. Predictive models often struggle with market unpredictability, while real-time systems must balance detection sensitivity with false positives. Furthermore, evolving financial regulations impose additional pressures on institutions to ensure that their systems are compliant. This paper concludes that while predictive models and real-time monitoring systems are essential for managing risks and detecting fraud, continuous innovation and collaboration between regulators and the financial industry are needed to keep pace with market dynamics.