Predictive Visual Analytics for Financial Anomaly Detection: A Big Data Framework for Proactive Decision Support in Volatile Markets

Abstract

This paper presents a novel predictive visual analytics framework for financial anomaly detection designed to provide proactive decision support in volatile market environments. Traditional anomaly detection systems face significant challenges in dynamic financial markets, including high data velocity, complex pattern recognition requirements, and stringent privacy constraints. The proposed framework addresses these challenges through a multi-layered architecture that integrates privacy-preserving data processing with advanced visualization techniques and predictive analytics. The architecture incorporates homomorphic encryption for secure computation while maintaining processing capacity of 75,000 encrypted operations per second. Experimental evaluation across diverse financial datasets demonstrates detection accuracy improvements of 8.7-14.2% compared to benchmark systems while reducing detection latency by 27.3%. The multi-dimensional visualization models enable analysts to identify complex relationships between financial entities across temporal dimensions, with domain experts rating structural comprehensibility 42% higher than conventional approaches. Case studies involving real-world financial anomaly scenarios confirm the framework's effectiveness, with early detection advantages of 7.3 minutes for market manipulation patterns. The research contributes a comprehensive approach to financial anomaly detection that balances analytical performance with data security requirements, enabling financial stakeholders to make more informed decisions in increasingly volatile market conditions.