TLDR: Insurers are facing ‘uncertainty loading’ of 5% to 10% on reinsurance premiums, amounting to an estimated £25 billion annually, due to unclear or inconsistent data submissions. This issue is particularly critical as the industry increasingly adopts Artificial Intelligence, which requires robust data foundations to avoid significant financial penalties and operational risks.
Reinsurance premiums are seeing a significant uptick, with ‘uncertainty loading’ adding between 5% and 10% to costs for cedents, according to Ben Rose, president of reinsurance technology platform firm Supercede. This surcharge, driven by the submission of unclear or inconsistent data to reinsurers, is estimated to cost the sector approximately £25 billion annually, simply due to inadequate data management.
Speaking at RVS 2025 in Monte Carlo, Rose highlighted an ‘inbuilt danger’ impacting primary market firms that are integrating Artificial Intelligence (AI) into their operations. He explained that when reinsurance underwriters receive submission information that ‘doesn’t make sense or looks inconsistent with last year,’ their only safe recourse is to ‘charge a higher price – just in case.’ This precautionary measure directly translates into the ‘uncertainty loading’ observed in premiums.
Rose emphasized the critical need for ‘solid data’ to underpin AI tools in the reinsurance industry. He warned that attempting to implement AI without a reliable source of primary data is ‘akin to building a house on very shaky foundations.’ He noted that the reinsurance industry’s reliance on data from the primary insurance market, which is often unreliable or inaccurate, poses a significant challenge.
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This issue is not merely an operational hurdle but a substantial financial burden. Rose pointed out that a reinsurance spend of a few hundred million pounds can escalate by £10 million, or even up to an extra £100 million, solely because of data ambiguity. The core message from Supercede is a stark warning against rushing into AI adoption without first establishing a robust and accurate data infrastructure, as the financial implications of ‘uncertainty loading’ are already substantial and are exacerbated by the demands of AI.
