Synergizing Human Expertise and Data Engineering: A Modern Framework for Financial Fraud Detection

Abstract

This comprehensive article explores the integration of human expertise with data engineering frameworks in modern financial fraud detection systems. The article examines the evolution from traditional rule-based approaches to sophisticated hybrid systems that leverage human analytical capabilities and advanced machine-learning techniques. The article presents a detailed analysis of technical infrastructure requirements, data pipeline architectures, and the crucial human-machine collaboration model that forms the backbone of effective fraud detection. It shows the implementation of detection and analysis frameworks while addressing the challenges and risk management considerations in maintaining such systems. Through extensive examination of current research and industry practices, this article demonstrates how the synergy between human domain expertise and technological capabilities enhances fraud detection accuracy, reduces false positives, and improves overall system efficiency. The findings emphasize the importance of continuous adaptation and evolution in fraud prevention strategies, highlighting how organizations can effectively combine human insight with artificial intelligence to create robust, scalable, and proactive fraud detection systems.