AI-Augmented Blockchain Analytics: Fraud Detection, Smart Contract Risk Scoring, and Trustworthy Decentralized Intelligence
Main article
Abstract
The convergence of artificial intelligence (AI) and blockchain has produced a new class of analytical systems that observe, interpret, and act on decentralized ledger activity. This review examines AI-augmented blockchain analytics across three tightly coupled capability areas: transactional fraud detection, smart-contract risk scoring, and trustworthy decentralized intelligence. Drawing on 60 peer-reviewed studies published between 2015 and 2025, supplemented by aggregated benchmark results from 312 documented deployments, the paper proposes a three-layer architectural framework that links on-chain data ingestion, learning-based inference, and decision-grade application services. Comparative evaluation across six families of detection models shows that graph-aware and hybrid architectures achieve median F1 gains of 0.18-0.24 over conventional tabular baselines, with the strongest gains observed in adversarial DeFi scenarios. For smart-contract auditing, deep semantic models combined with symbolic execution recover roughly 91% of disclosed vulnerabilities in established corpora, while reducing false-positive rates by a factor of two to three. The review further quantifies the latency and throughput envelope of centralized, federated, and on-chain inference deployments, and identifies governance, explainability, and adversarial robustness as the most consequential constraints on trustworthy decentralized intelligence. The paper concludes by outlining a research agenda that prioritizes verifiable AI primitives, privacy-preserving federated analytics, and standards-aligned audit infrastructure through 2028.
