article · International Journal of Scientific Research and Modern Technology.
Decentralised finance has reshaped financial ecosystems through transparent, automated, and permissionless investment models. Despite these advances, the sector faces critical hurdles concerning market volatility, regulatory ambiguity, and complex data environments. Integrating artificial intelligence models, including machine learning, deep learning, and natural language processing, offers predictive tools to identify fraudulent transactions, discover smart contract vulnerabilities, and manage investment portfolio risks proactively. Furthermore, intelligent analytics can assist platforms in aligning with regulatory requirements such as anti-money laundering, know-your-customer rules, and data privacy legislation. Incorporating explainable artificial intelligence ensures that predictive outputs remain transparent and interpretable for both institutional regulators and individual investors. Synthesising current literature and practical use cases demonstrates how predictive computational frameworks can reconcile digital financial innovation with governance mandates, fostering systemic stability and investor security.
Decentralised financial platforms handle substantial capital without centralised intermediaries, making them vulnerable to financial crimes and security exploits. Demonstrating how artificial intelligence can forecast risks and satisfy legal compliance without undermining decentralisation provides a pathway for mainstream institutional adoption, safer investment environments, and clearer alignment with international financial oversight bodies.
The proposed predictive frameworks could be implemented in automated risk management tools, compliance monitoring software, and smart contract auditing platforms. Primary users include decentralised finance developers, compliance officers, and regulatory bodies. As this work is a literature review and synthesis presenting a conceptual framework, the technology is at an early research stage, requiring software development and empirical deployment before market readiness.
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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.
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DOI: 10.38124/ijsrmt.v4i11.1028
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