Impact Analysis of Hyperledger Fabric Blockchain and Machine Learning on the Creation of Safe and Fraud-proof Financial Systems

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.