In the modern complex trading environment, trading risk management is facing unprecedented challenges. The research of anomaly detection and transaction risk management technology based on machine learning, by optimizing the traditional risk management model and introducing machine learning algorithm, improves the accuracy of risk prediction, the intelligence of trading strategy and the real-time monitoring and early warning. This paper discusses the optimal design of transaction risk management model, including the construction of risk prediction, evaluation model, trading strategy and risk control. This paper analyzes the anomaly detection methods based on machine learning, focusing on statistics-driven anomaly detection, machine learning algorithm and anomaly recognition technology based on neighborhood relationship. The results show that machine learning techniques can significantly improve the efficiency and precision of risk management, helping to deal with uncertainty in complex markets.

Shuang Yuan. Research on Abnormal Detection and Transaction Risk Management Based on Machine Learning. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 10-18. https://doi.doi.org/10.3807/IJSSEM.2026.070102.

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