Mitigating systemic risk in catastrophe insurance: The role of human judgment in model diversification
Type
conference contribution
Date Issued
2025-05-20
Author(s)
Abstract
This study aims to investigate the systemic risk implications of widespread reliance on
standardized vendor catastrophe models in the insurance and reinsurance industry. While these
models guide critical decisions around capital allocation and risk transfer, their uniform use may lead
to homogenized risk assessments and increased systemic vulnerability. Using empirical analysis of
model outputs, actual loss data, and human adjustments on the model output, we aim to evaluate
whether expert interventions can diversify risk evaluations and mitigate systemic risk. Our findings
will contribute to the growing discourse on human-in-the-loop decision-making, examining the value
of expert judgment in counterbalancing algorithmic biases.
standardized vendor catastrophe models in the insurance and reinsurance industry. While these
models guide critical decisions around capital allocation and risk transfer, their uniform use may lead
to homogenized risk assessments and increased systemic vulnerability. Using empirical analysis of
model outputs, actual loss data, and human adjustments on the model output, we aim to evaluate
whether expert interventions can diversify risk evaluations and mitigate systemic risk. Our findings
will contribute to the growing discourse on human-in-the-loop decision-making, examining the value
of expert judgment in counterbalancing algorithmic biases.
Event Title
Insurance Data Science Conference
Event Location
London
Event Date
19 - 20 June 2025