AI Powered Predictive Frameworks for Risk Modeling and Regulatory Compliance in Decentralized Finance Investment Systems

Abstract

The emergence of decentralized finance (DeFi) has transformed global financial ecosystems by enabling transparent, permissionless, and automated investment systems. However, the inherent volatility, regulatory uncertainty, and data complexity within DeFi ecosystems pose significant challenges for risk modeling and compliance assurance. This review explores the integration of AI-powered predictive frameworks to enhance risk assessment, fraud detection, and regulatory compliance in decentralized finance investment systems. By leveraging machine learning (ML), deep learning (DL), and natural language processing (NLP) models, the study examines how predictive analytics can proactively identify anomalous transactions, assess smart contract vulnerabilities, and optimize portfolio risk exposure. The paper also evaluates how AI-driven systems can align DeFi operations with emerging regulatory frameworks, including KYC/AML protocols, data protection standards, and algorithmic auditing requirements. Additionally, the review highlights the role of explainable AI (XAI) in promoting transparency, interpretability, and trust among regulators and investors. Through a synthesis of existing literature and real-world applications, this paper presents a comprehensive framework illustrating how predictive AI technologies can bridge the gap between financial innovation and regulatory governance in DeFi. The findings underscore the potential of intelligent, adaptive, and compliant DeFi systems capable of ensuring sustainable growth, investor protection, and systemic stability in the evolving digital financial landscape.