Real-time Anomaly Detection in Financial Trading Systems: An Adaptive Approach to Mitigating Trading Errors

Abstract

This paper presents an advanced real-time anomaly detection system designed to identify and mitigate trading errors in financial markets. Our system leverages statistical analysis, dynamic thresholds, and periodic batch processing to enhance accuracy and reduce false positives. By comparing new orders against historical data and instrument-specific thresholds, we have successfully caught erroneous trades worth millions, potentially saving significant losses for trading firms. The system utilizes Spring Batch for efficient processing and incorporates adaptive mechanisms to handle various security types and rare order scenarios. This paper details our methodology, implementation, and results, providing insights into effective risk management in high-stakes financial trading environments.