Intelligent Decentralized Insurance: Analytical Perspectives on Automated Risk and Trust Systems
Main article
Abstract
Decentralized insurance combines distributed-ledger infrastructure, self-executing contracts, and machine intelligence to perform risk pooling, pricing, and claim settlement without a single coordinating intermediary. This paper develops an analytical perspective on such intelligent decentralized insurance, asking how automation reshapes the cost structure of risk transfer and how trust is produced when no central insurer stands behind the promise to pay. A four-plane reference architecture is proposed that separates participants and assets, data and oracles, automation logic, and governance assurance, and three families of automated risk architectures are examined through this lens: algorithmic mutual pools, parametric trigger contracts, and token-governed coverage protocols. The analysis is supported by a structured synthesis of peer-reviewed research published between 2016 and 2024 and by stylized quantitative models. The expense decomposition indicates that automation can plausibly compress operating loads from roughly 29 percent of premium toward 12 percent, while settlement latency falls from weeks to minutes for fully automated parametric designs. A pool-stability model shows, however, that loss dependence places a hard floor under the solvency benefits of scale, and an oracle redundancy model quantifies how trigger integrity improves geometrically with independent data sources. The paper argues that the binding constraints on intelligent decentralized insurance are no longer purely technological: they concern data integrity, contract security, governance legitimacy, and regulatory recognition. A research agenda is outlined that links actuarial risk-sharing theory, information systems design, and institutional economics.
