Blockchain and AI Integration for Fraud Detection in Financial Systems: A Comprehensive Review and Technical Framework

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Abstract The exponential growth of digital financial transactions has intensified the sophistication and frequency of fraudulent activities, necessitating advanced technological interventions beyond traditional rule-based systems. This paper presents a comprehensive review and technical framework for integrating blockchain technology with artificial intelligence (AI) to enhance fraud detection capabilities in financial systems. We systematically analyse 30 highly relevant studies published between 2022 and 2026, examining the synergistic integration of blockchain's immutable ledger capabilities with AI's pattern recognition and anomaly detection strengths. Our analysis reveals that hybrid blockchain-AI architectures achieve fraud detection accuracies ranging from 92% to 99.8%, significantly outperforming conventional methods. The review identifies four primary integration paradigms: (1) blockchain-secured AI model deployment, (2) federated learning with blockchain-based model aggregation, (3) smart contract-enabled automated fraud response, and (4) decentralized AI inference networks. We propose a novel theoretical framework grounded in information asymmetry theory and institutional trust theory, explaining how blockchain-AI integration addresses fundamental challenges in financial fraud detection. The paper critically examines technical architectures, including Ethereum-based smart contracts, Hyperledger Fabric implementations, and graph neural network approaches for transaction analysis. Key challenges identified include scalability limitations, computational overhead, privacy-preserving computation requirements, regulatory compliance complexities, and real-world deployment barriers. Our findings indicate that while blockchain-AI integration demonstrates substantial promise, successful implementation requires addressing interoperability standards, developing explainable AI mechanisms for regulatory compliance, and establishing robust governance frameworks. This work contributes to the theoretical understanding of technology-mediated trust in financial systems and provides practical guidance for implementing next-generation fraud detection architectures.

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