Impact Analysis of Hyperledger Fabric Blockchain and Machine Learning on the Creation of Safe and Fraud-proof Financial Systems
Year: 2025
Type: article
Abstract: This research investigates the integration of the permissioned Blockchain Hyperledger Fabric with machine learning (ML) to develop advanced financial fraud prevention strategies. Fabric's distinctive architecture uses execute-order-validate consensus, channels, and private data collections. This creates a secure, unchangeable, and sustainable data foundation. The design ensures transactional integrity and offers a reliable source of validated data for analytical processes. This high-quality data enables ML algorithms to work effectively. Supervised and ensemble models identify known fraud, while unsupervised methods, such as anomaly detection, find new threats in real-time, surpassing traditional systems. The primary effect arises from integrating these two technologies. Fabric's secure ledger offers the protected information needed to build trustworthy ML models. As a result, these advanced models let the network discuss threats and respond to suspicious behavior immediately. This merger forms a strong security framework. Fabric guarantees data integrity. Machine learning adds smart threat identification. Together, they provide financial institutions with a more innovative and dependable shield against fraud.
Authors: Jerome Steven S. Rosario, Ernesto M. Tolin III, Juliane Caye L. Pasteteo, Chynna F. Moreno, Pops V. Madriaga
Institution: Technological University of the Philippines
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7090158443
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