AI-Driven Risk Management and Fraud Detection in High-Frequency Trading Environments
Abstract: High-frequency trading (HFT) environments, characterized by the rapid execution of trades and large volumes of data, demand sophisticated and real-time risk management and fraud detection solutions. Traditional systems need help to keep up with the velocity and complexity of data, leaving gaps that can be exploited. This paper proposes an AI-driven architecture leveraging machine learning models to enhance risk management and fraud detection in HFT environments. Implemented using Amazon SageMaker for AI/ML processing, the architecture is designed to be scalable, efficient, and capable of real-time decision-making. The study presents the detailed architecture, discusses each component's role, and evaluates the system's performance in mitigating risks and detecting fraud in live trading scenarios.
Keywords: AI-driven risk management, fraud detection, high-frequency trading, machine learning, Amazon SageMaker, real-time data processing, predictive modeling, anomaly detection, algorithmic trading, decision support systems
How to Cite?: Yash Jani, "AI-Driven Risk Management and Fraud Detection in High-Frequency Trading Environments", Volume 12 Issue 11, November 2023, International Journal of Science and Research (IJSR), Pages: 2223-2229, https://www.ijsr.net/getabstract.php?paperid=SR24827092909, DOI: https://dx.doi.org/10.21275/SR24827092909