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This document analyzes the impact of model uncertainty (ambiguity) on the insurance industry.
The study employed a 𝗿𝗼𝗯𝘂𝘀𝘁 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 that assumes insurers adopt strategies to maximize value against a "worst-case" scenario. The views expressed are that this leads to a new competitive market equilibrium characterized by:
• 𝗦𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁𝗹𝘆 𝗵𝗶𝗴𝗵𝗲𝗿 𝗽𝗿𝗲𝗺𝗶𝘂𝗺𝘀 and 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗲𝗾𝘂𝗶𝘁𝘆 𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻𝘀.
• 𝗠𝗼𝗿𝗲 𝗰𝗼𝗻𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝘃𝗲 𝗹𝗶𝗾𝘂𝗶𝗱𝗶𝘁𝘆 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁, evidenced by higher precautionary reserves and delayed dividend payouts.
• 𝗦𝘂𝗯𝘀𝘁𝗮𝗻𝘁𝗶𝗮𝗹𝗹𝘆 𝗽𝗿𝗼𝗹𝗼𝗻𝗴𝗲𝗱 𝘂𝗻𝗱𝗲𝗿𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝗰𝘆𝗰𝗹𝗲, increasing in numerical simulations from 9.6 to 26 years.
• A long-run capacity distribution that is 𝗺𝗼𝗿𝗲 𝗰𝗼𝗻𝗰𝗲𝗻𝘁𝗿𝗮𝘁𝗲𝗱 𝗶𝗻 𝗹𝗼𝘄-𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆 𝘀𝘁𝗮𝘁𝗲, implying slower recovery from adverse shocks.
The paper suggests these findings offer a theoretical explanation for the difficulty of detecting underwriting cycles in empirical data.

Insurance decisions range from trivial to significant, accumulating impact over time. Intuition can mislead, especially when premiums rise due to risk. Key factors include hazard size, wealth, risk aversion, and insurer margins. Greater transparency in insurance margins can help families make informed choices, improving financial well-being and societal welfare.

This article also has links to a calculator and spreadsheet which apply the framework described herein.

This study examines the use of #artificialintelligence (#ai) and #bigdata data analytics by #insurers in #belgium for segmentation purposes to determine #claims#probability for prospective policyholders. The implementation of AI and big data analytics can benefit insurers by increasing the accuracy of #riskassessment. However, pervasive segmentation can have negative implications and potentially harm policyholders if their risk is incorrectly calculated. Existing restrictions in #insurance#regulations fall short of protecting policyholders from inaccuracies in risk assessments, potentially resulting in incorrect #premiums or conditions.

"The standard statistical approaches to assessment of insurability and potential mispricing are enhanced in several aspects involving consideration of model risk … We demonstrate how to quantify the effect of model risk in this analysis by incorporating various robust estimators for key model parameter estimates that apply in both marginal and joint cyber risk loss process modelling."