AI-Based Smart Contract Analysis for Digital Transactions

Abstract

Blockchain technology has transformed digital transactions into decentralized, transparent, and immutable systems. Smart contracts running on platforms like Ethereum enable automated agreements without intermediaries, but they remain vulnerable to logical errors and security risks that can lead to financial losses. This paper presents a systematic study on AI-based smart contract analysis, using machine learning and deep learning to detect vulnerabilities, anomalies, and potential risks. It proposes a hybrid model combining static analysis (code-level error detection), dynamic analysis (runtime monitoring), and supervised/unsupervised learning techniques. Feature extraction methods convert contract code into formats suitable for AI processing. The approach integrates rule-based systems with AI models to improve detection accuracy and reduce false positives. Evaluation on benchmark datasets shows better performance than traditional methods. The study highlights the effectiveness of AI in enhancing smart contract security and suggests future work on explainable AI, real-time monitoring, and cross-platform interoperability.