AI-driven anomaly detection in real-time streaming: enhancing human decision-making

Mukkath, Shakir Poolakkal (2025) AI-driven anomaly detection in real-time streaming: enhancing human decision-making. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 410-420. ISSN 2582-8266

AI-driven anomaly detection in real-time streaming data has emerged as a transformative approach for organizations across industries facing unprecedented volumes of information. Traditional rule-based monitoring systems struggle with the complexity and evolving nature of modern data streams, often generating excessive false positives and missing subtle patterns that indicate fraud, system failures, or security breaches. This article examines how machine learning models integrated into streaming pipelines can enhance human decision-making by processing massive data volumes while identifying anomalies that would be impossible to detect manually. The technical foundations of real-time detection are explored, including stream processing architectures and various machine learning approaches such as statistical methods, unsupervised learning, and online algorithms. Implementation strategies for feature engineering, concept drift management, and latency optimization are discussed alongside industry applications in telecommunications, banking, retail, and cybersecurity. The article emphasizes that the most effective anomaly detection systems combine AI's pattern recognition capabilities with human expertise in a collaborative partnership, where machines handle data processing at scale while humans provide domain knowledge, contextual understanding, and strategic direction. This symbiotic relationship, supported by explainable AI and adaptive alert management, creates detection capabilities far superior to either humans or machines operating independently.