Secure and Scalable Financial Fraud Detection via Blockchain and Hybrid Cloud Technologies: A Systematic Review

Author: KhushiPatel, Khyati Prajapati
Published Online: January 5, 2026
Abstract
References

The quick emergence and evolution of digital financial services have given rise to high-return crimes like credit card fraud, money laundering, and various scams through digital payments, which in turn have posed severe economic and security challenges. Traditional anti-fraud systems often struggle with scalability, flexibility, and response, rendering them ineffective in countering sophisticated cyber attacks. Recent advances in blockchain, hybrid cloud computing, and artificial intelligence (AI) provide promising directions for designing secure and scalable fraud detection frameworks. The blockchain provides secure and traceable transactions, and hybrid cloud systems facilitate rapid and flexible processing of substantial financial data. AI- driven models, incorporating methods such as federated learning and explainable AI, also enhance anomaly detection, data security, and interpretability in anti-fraud systems. This review synthesizes contributions, focusing on the merging of blockchain, hybrid cloud, and AI for the detection of financial fraud. Key approaches, advantages, limitations, and open challenges, such as interoperability, regulatory compliance, and scalability bottlenecks, are critically analyzed. The study also highlights future opportunities, including federated learning and quantum-ready architectures, to support secure, efficient, and trustworthy financial ecosystems.

Keywords: Financial Fraud Detection, Blockchain Technology, Hybrid Cloud Computing, Artificial Intelligence, Federated Learning, Explainable AI, Quantum-Inspired Security, Scalable Systems..
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