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

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.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-31
DOI
https://doi.org/10.5281/zenodo.17617174
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
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article

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

Zenodo (CERN European Organization for Nuclear Research)
Blockchain Technology Applications and Security
article

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

article en

Abstract

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.

Zenodo (CERN European Organization for Nuclear Research)
Qatar Credit Bureau (QA)
Industry, innovation and infrastructure
Openalex Percentile: Top 31%
Blockchain Technology Applications and Security
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Leveraging AI-powered data streams for predictive risk assessment in cross-protocol defi lending platforms · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS