Leveraging AI-powered data streams for predictive risk assessment in cross-protocol defi lending platforms

Davor, Sandra (2025) Leveraging AI-powered data streams for predictive risk assessment in cross-protocol defi lending platforms. World Journal of Advanced Research and Reviews, 27 (2). pp. 315-334. ISSN 2581-9615

The rapid evolution of decentralized financial (DeFi) ecosystems has introduced unprecedented flexibility, transparency, and user control in global capital markets. However, this decentralization also introduces considerable systemic vulnerabilities, including fragmented data landscapes, unregulated liquidity flows, and an increased surface area for fraud, volatility, and contagion effects. Traditional risk assessment frameworks, built for centralized and structured financial environments, lack the adaptability and responsiveness required to capture these nonlinear, rapidly shifting risk factors in real time. This paper explores how artificial intelligence (AI), particularly advanced machine learning and graph-based inference techniques, can be leveraged to integrate diverse, unstructured datasets from blockchain ledgers, smart contracts, market oracles, and social media to construct comprehensive and predictive risk assessment models for DeFi environments. We propose an AI-driven data integration framework that combines entity recognition, transactional pattern mining, and sentiment-weighted event analysis to model user behavior, asset interdependencies, and protocol vulnerabilities. Emphasis is placed on anomaly detection for early warning signals of flash loan attacks, liquidity drain risks, and cascading failures across interconnected DeFi protocols. Case studies are used to demonstrate the system’s applicability to real-world DeFi incidents, such as stablecoin de-pegging events and governance manipulation. The results highlight the framework's effectiveness in reducing response latency, improving capital resilience, and enhancing portfolio risk transparency for developers, auditors, and institutional investors. Furthermore, this approach fosters proactive regulatory insight by mapping systemic risks without compromising the decentralized ethos. By fusing AI with decentralized data architectures, this work contributes a novel paradigm for safeguarding the integrity and sustainability of next-generation financial infrastructures.