ZestyAI, a provider of an AI-driven risk decision platform for the insurance sector, has published research suggesting that patchy and out-of-date property information is leaving insurers paying an ‘uncertainty premium’ in the reinsurance market, with costs reflecting missing knowledge rather than the true quality of their risk.

The findings come from a live poll held during an InsTech webinar, in which 71% of insurance professionals named incomplete or stale exposure data as their main obstacle when assessing property catastrophe risk. Far fewer pointed to conflicting stakeholder perspectives on risk (13%), problems verifying third-party data (11%) or trouble separating risks within comparable portfolios (3%). According to the accompanying whitepaper, 94 people answered this question. In a follow-up poll on what would make third-party property data more trustworthy, half opted for independent validation, while one third favoured proven predictive performance.

In its whitepaper, The Uncertainty Premium, ZestyAI contends that such shortfalls carry real consequences for reinsurance. Where reinsurers are unable to confirm key details about properties within a cedant’s book, they may rely on cautious default settings in catastrophe models. These assumptions can push modelled losses higher, which in turn may influence pricing, attachment points or the capacity on offer.

This means insurers holding higher-quality books may not be fully rewarded for them, while reinsurers can find it hard to tell strong portfolios apart from weak ones. Both parties lose out: cedants may be charged for uncertainty unrelated to their actual risk, and reinsurers may misprice books they cannot properly distinguish.

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