A Secure AI Architecture for Dynamic Credit Scoring in DeFi Environments

Abstract

The rapid expansion of decentralized finance (DeFi) has intensified the need for adaptive, transparent, and secure credit scoring mechanisms capable of operating without centralized intermediaries. Traditional credit evaluation models,constrained by siloed data sources, static risk metrics, and opaque decision processes,are insufficient for dynamic, on-chain lending ecosystems. This paper proposes a Secure AI Architecture for Dynamic Credit Scoring in DeFi, integrating privacy-preserving machine learning, blockchain-anchored data provenance, and cross-platform financial data unification. Building on emerging research on AI in DeFi ecosystems, risk assessment, and responsible credit scoring frameworks, the architecture employs federated learning, encrypted feature extraction, and verifiable smart-contract execution to ensure robustness, explainability, and regulatory alignment. The model continuously ingests heterogeneous on-chain and off-chain signals, mitigating fraud and enhancing predictive power in volatile market conditions. Smart-contract-based governance ensures auditability and automated compliance, while decentralized risk oracles facilitate tamper-resistant model updates. By synthesizing advances in AI-driven credit analytics, cross-platform data integration, and blockchain-enabled loan automation, this work presents a scalable and secure design suited for next-generation DeFi lending platforms. The proposed architecture addresses current limitations in trust, transparency, and adaptability, offering a blueprint for ethically aligned, data-driven credit systems in decentralized financial markets.