Ship insurance in the era of AI: An intelligent risk profiling system under the POM principles

Abstract As the origin of modern commercial insurance, ship insurance underpins global maritime supply chain stability. Yet shipping modernization and AI advances expose three flaws in traditional risk profiling: misalignment with frequency‐severity pricing, inadequate for accommodating to complex risk factor system, and lack of data stream adjustment mechanisms. To address these, we propose POM principles ( Personalized risk portrait , Omnispective risk factors , and Maneuverable calibration ) and an AI framework with three cores: (1) extensible “retrospective + prospective” risk factors; (2) independent AI modules for premium rate/insurance amount prediction; (3) data steam calibration on historical data. Validated via 15,007 records (15% of China's 2016–2021 registered ships) using random forest regression, it outperforms traditional generalized linear models and mainstream machine learning models in accuracy and risk differentiation. This pioneers intelligent ship insurance profiling, fills gaps in individualized pricing, and offers insights for sectors like aviation insurance, sharing its “premium rate × insurance amount” logic.