A Systematic Review on the Application of Artificial Intelligence in Decentralized Finance

This study presents a comprehensive systematic review of Artificial Intelligence (AI) applications in DecentralizedFinance (DeFi), emphasizing AI’s pivotal role in mitigating the vulnerabilities and operational complexities inherentin permissionless financial systems. By systematically analyzing 39 peer-reviewed studies from major scholarlydatabases, the review identifies five dominant application domains: fraud detection, smart contract security, marketprediction, credit risk assessment, and decentralized governance. It examines the diverse range of AI methodsspanning machine learning, deep learning, graph neural networks, and reinforcement learning—and evaluates theircomparative performance and limitations. The findings reveal that AI not only enhances DeFi’s transparency, trust,and efficiency but also underpins emerging capabilities such as autonomous governance and adaptive marketmechanisms. Persistent challenges including data scarcity, cross-chain generalization, interpretability, andscalability—underscore the need for robust, explainable, and ethical AI solutions. The review concludes that AIconstitutes a foundational enabler for secure, transparent, and resilient decentralized financial ecosystems, andoutlines critical future research directions for integrating trustworthy intelligence into the evolving DeFi landscape. (PDF) A Systematic Review on the Application of Artificial Intelligence in Decentralized Finance. Available from: https://www.researchgate.net/publication/397514996_A_Systematic_Review_on_the_Application_of_Artificial_Intelligence_in_Decentralized_Finance [accessed Nov 11 2025].

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-06
DOI
https://doi.org/10.5281/zenodo.17584874
Primary Topic
FinTech, Crowdfunding, Digital Finance
Type
article
Field-Weighted Citation Impact
0.00
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article

A Systematic Review on the Application of Artificial Intelligence in Decentralized Finance

Zenodo (CERN European Organization for Nuclear Research)
FinTech, Crowdfunding, Digital Finance
article

A Systematic Review on the Application of Artificial Intelligence in Decentralized Finance

article en

Abstract

This study presents a comprehensive systematic review of Artificial Intelligence (AI) applications in DecentralizedFinance (DeFi), emphasizing AI’s pivotal role in mitigating the vulnerabilities and operational complexities inherentin permissionless financial systems. By systematically analyzing 39 peer-reviewed studies from major scholarlydatabases, the review identifies five dominant application domains: fraud detection, smart contract security, marketprediction, credit risk assessment, and decentralized governance. It examines the diverse range of AI methodsspanning machine learning, deep learning, graph neural networks, and reinforcement learning—and evaluates theircomparative performance and limitations. The findings reveal that AI not only enhances DeFi’s transparency, trust,and efficiency but also underpins emerging capabilities such as autonomous governance and adaptive marketmechanisms. Persistent challenges including data scarcity, cross-chain generalization, interpretability, andscalability—underscore the need for robust, explainable, and ethical AI solutions. The review concludes that AIconstitutes a foundational enabler for secure, transparent, and resilient decentralized financial ecosystems, andoutlines critical future research directions for integrating trustworthy intelligence into the evolving DeFi landscape. (PDF) A Systematic Review on the Application of Artificial Intelligence in Decentralized Finance. Available from: https://www.researchgate.net/publication/397514996_A_Systematic_Review_on_the_Application_of_Artificial_Intelligence_in_Decentralized_Finance [accessed Nov 11 2025].

Zenodo (CERN European Organization for Nuclear Research)
Stevens Institute of Technology (US)
Decent work and economic growth
Openalex Percentile: Top 31%
FinTech, Crowdfunding, Digital Finance
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