Big Data Analytics in Digital Banking Fraud Detection Technologies and Methods
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Abstract
Digital banking fraud has escalated dramatically with the proliferation of online financial services, causing billions in annual losses and threatening the stability of global economic systems. This paper examines the Big Data Analytics (BDA) technologies and methods for real-time fraud detection in digital banking environments. The paper discusses the evolution from traditional rule-based systems to modern distributed computing frameworks, analysing how Apache Hadoop and Spark enable the processing of massive transaction volumes with varying trade-offs between latency and accuracy. Key machine learning approaches are covered, including supervised methods, unsupervised methods, and hybrid architectures that combine both paradigms. The paper identifies critical implementation challenges across technical dimensions, operational aspects, and regulatory requirements. Emerging trends explored include federated learning for privacy-preserving model training, blockchain integration for cross-institutional fraud detection, and edge computing for ultra-low latency inference. The analysis shows that while individual studies report improvements in detection, challenges remain in real-world validation, model interpretability, and cross-institutional generalizability. The paper concludes with practical recommendations for implementing hybrid streaming-batch architectures, embedding explainability mechanisms, and adopting privacy-preserving techniques. This paper provides insight for researchers and practitioners to understand the current capabilities, limitations, and future trends of BDA in enhancing fraud detection in increasingly complex digital banking ecosystems.
Manuscript received: 9 Jun 2025 | Revised: 6 Aug 2025 | Accepted: 24 Sep 2025 | Published: 31 Mar 2026
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