BLOCKCHAIN-AUGMENTED FEDERATED AI: STRENGTHENING SECURITY AND TRUST IN THE FINANCIAL SECTOR
Abstract
The Banking, Financial Services, and Insurance (BFSI) sector is rapidly adopting advanced digital technologies to enhance fraud detection, risk management, and regulatory compliance. However, traditional centralized AI systems face critical challenges, including data privacy concerns, vulnerability to tampering, and a lack of auditability. To address these issues, this paper proposes a Blockchain-Integrated Federated AI Framework that combines the predictive power of Artificial Intelligence with the security and transparency of blockchain technology. The framework is structured into five core layers: (1) Data Acquisition, (2) Federated AI Training, (3) Blockchain Ledger, (4) Consensus and Incentive, and (5) Security and Compliance. Through federated learning, multiple financial institutions collaborate to train shared AI models without exposing sensitive data, while blockchain ensures tamper-proof recording, verifiable contributions, and decentralized trust. Experimental evaluation shows a 4.2% improvement in accuracy, over 95% tamper detection rate under adversarial attacks, and linear latency overhead, proving the framework’s robustness and scalability. This integrated approach supports secure and compliant AI deployments in high-stakes financial applications such as fraud detection, credit risk scoring, and AML/KYC compliance, laying the foundation for trustworthy and intelligent financial ecosystems.
Keywords
Artificial Intelligence (AI); Blockchain; Federated Learning; BFSI; Fraud Detection; Risk Management; Secure Data Sharing; Tamper-Proof Ledger; Consensus Mechanism; Regulatory Compliance.