Work

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

Year: 2025

Type: article

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.

Source: Journal of Risk & Insurance

DOI: 10.1111/jori.70033

Authors: Fangping Yu, Mengdan Zhang, Zhengxiao Li, Mo Yang

Institutions: Dalian Maritime University, University of International Business and Economics, Dongbei University of Finance and Economics

Language: English

Topic: Insurance and Financial Risk Management

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

Funders: National Natural Science Foundation of China, Ministry of Education of the People's Republic of China, National Social Science Fund of China