OPTIMIZING REAL-TIME DATA PIPELINES FOR FINANCIAL FRAUD DETECTION: A SYSTEMATIC ANALYSIS OF PERFORMANCE, SCALABILITY, AND COST EFFICIENCY IN BANKING SYSTEMS

Abstract

The proliferation of digital financial transactions has intensified the need for sophisticated real-time fraud detection systems within banking institutions. This article presents a systematic analysis of real-time data pipeline optimization strategies for financial fraud detection, addressing critical challenges in performance, scalability, and cost efficiency. Through a comprehensive examination of stream processing architectures, we evaluate various optimization techniques across the data pipeline lifecycle, from ingestion to analytical processing. The article methodology combines theoretical analysis with practical implementation insights, examining cloud-native architectures and machine learning integration approaches. The findings demonstrate that optimized real-time pipelines significantly enhance fraud detection capabilities while maintaining system efficiency. The article reveals key patterns in latency reduction, resource utilization, and cost management, providing valuable insights for financial institutions implementing similar systems. Furthermore, the article presents a framework for evaluating and implementing optimization strategies, considering factors such as data volume variability, processing complexity, and infrastructure scalability. This article contributes to the growing body of knowledge in financial technology by establishing best practices for real-time fraud detection system implementation and offering practical recommendations for future developments in the field.