What does this research mean for the field?

Integrating distributed big data analytics, artificial intelligence, graph neural networks, and blockchain into a unified framework provides financial institutions with a more scalable, transparent, and adaptive approach to fraud prevention and risk management. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To propose an integrated distributed analytics framework combining artificial intelligence, machine learning, and blockchain for scalable fraud prevention and risk management in financial systems.

August 27, 2026Open Access

A Distributed Intelligent Analytics Framework for Blockchain-Based Fraud Detection and Risk Management in Financial Institutions

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To propose an integrated distributed analytics framework combining artificial intelligence, machine learning, and blockchain for scalable fraud prevention and risk management in financial systems.
Designed an architecture integrating distributed big data analytics, artificial intelligence, machine learning, and intelligent decision support engines.
Incorporated Graph Neural Networks to model interconnected relationships among customers, accounts, devices, merchants, and financial transactions.
Embedded a blockchain layer to provide decentralized integrity, traceability, and verification for transaction validation.
Resolved structural data challenges including processing latency, extreme class imbalance, concept drift, and data heterogeneity in high-volume environments.
Enabled the detection of complex, multi-entity fraud networks that remain undetectable through isolated, transaction-level analyses.
Provided an adaptable and transparent operational paradigm aligning automated cybersecurity defenses with strict regulatory compliance frameworks.

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Implication

Architectural analysis reveals a distributed blockchain and machine learning framework for financial institutions, indicating enhanced fraud detection scalability and risk management.

Cite This Study

Arnult Michael (2026) studied this question.

synapsesocial.com/papers/6a8fea0d10c91c1e92622110 https://doi.org/10.5281/zenodo.22088353

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Implication

Architectural analysis reveals a distributed blockchain and machine learning framework for financial institutions, indicating enhanced fraud detection scalability and risk management.

A Distributed Intelligent Analytics Framework for Blockchain-Based Fraud Detection and Risk Management in Financial Institutions

Key Points

Discussion

Implication

Cite This Study

A Distributed Intelligent Analytics Framework for Blockchain-Based Fraud Detection and Risk Management in Financial Institutions

Key Points

Discussion

Implication

Cite This Study

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