AI-Enabled Distributed Big Data Framework for Blockchain-Based Fraud Prevention, Regulatory Compliance, and Financial Risk Prediction
Year: 2025
Type: preprint
Abstract: The rapid expansion of digital banking, financial technology, mobile payments, and blockchain-based financial services has created an unprecedented volume of financial data. While these data provide opportunities for intelligent decision-making, they also introduce significant challenges related to fraud prevention, regulatory compliance, cybersecurity, and financial risk management. Traditional centralized and rule-based systems often struggle with the scale, heterogeneity, velocity, and dynamic nature of modern financial transactions. This paper proposes an AI-enabled distributed big data framework for blockchain-based fraud prevention, regulatory compliance, and financial risk prediction. The framework integrates distributed data acquisition, scalable data processing, artificial intelligence, machine learning, blockchain verification, anomaly detection, regulatory intelligence, and predictive risk analytics. Distributed big data technologies provide the computational foundation for processing high-volume heterogeneous financial datasets, while artificial intelligence identifies suspicious patterns and predicts emerging risks. Blockchain provides transaction integrity, transparency, traceability, and an auditable foundation for compliance activities. The framework builds on Ramareddy's (2025) work on distributed big data analytics for scalable knowledge discovery and Chhunchha's (2025) examination of blockchain technology in banking institutions. Recent studies demonstrate that AI and blockchain can improve fraud detection, risk assessment, and compliance-oriented financial monitoring, although challenges involving privacy, scalability, explainability, data imbalance, regulatory governance, and model drift remain. The proposed framework addresses these challenges through a layered architecture that combines distributed analytics with intelligent blockchain-enabled decision support. The study concludes that integrating AI, distributed big data, and blockchain can provide financial institutions with a scalable and adaptive infrastructure for preventing fraud, strengthening regulatory compliance, and predicting financial risks.
Author: Phillips, Thomas
Language: ang
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204122161