Automated Anomaly Detection in High-Dimensional Financial Transaction Data Using Hybrid Hypervector and Gaussian Mixture Models

**Abstract:** Detecting anomalous financial transactions in real-time is critical for fraud prevention and maintaining financial stability. Traditional methods struggle with high-dimensional data and evolving fraud patterns. This paper introduces a novel approach, Hybrid Hypervector Gaussian Mixture (HH-GMM), combining the efficiency of hypervector networks for pattern recognition with the robust statistical modeling of Gaussian Mixture Models (GMMs) to achieve superior anomaly detection accuracy. HH-GMM demonstrates scalability and adaptability to evolving dataset characteristics, promising significant improvements in real-time fraud detection systems. Projection of potential impact within financial institutions shows a 15-20% reduction in fraudulent activity and an estimated market size of $5B+ within 5 years.