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Identifying a new data partner to reduce underinsurance

Challenge

A large P&C insurer was looking for a new property data provider to enhance its underwriting process. The commercial underwriting and product team was interested in property attribute information and rebuild values in particular, aiming to tackle the challenge of underinsurance within its portfolio.

Whilst the insurer knew about certain companies offering property data, it was unsure which ones provided information on commercial properties. They wanted to filter out the companies that would not be able to meet their needs and explore those which could fill their current data gaps.

Solution

The insurer initially read our ‘Property Intelligence: the where and what’ report to understand more about the property data landscape. The report explores the recent advancements in technology for acquiring building characteristics and rebuild values, the use of new data sources from outside of insurance and the rise of third-party data consolidators.

We then set up a briefing call to further discuss the topic. This enabled us to dive deeper into the trends we have been seeing in the space, giving the insurer opportunities to ask us questions. This also helped us to understand the property information the insurer was looking for.

After this call we used ATLAS, our data and technology company database, to identify companies relevant to the insurer’s needs. The output was provided to the insurer, which included various information fields such as property data type, existing insurance clients and InsTech’s awareness of the companies. This could be used to identify where we could directly help with introductions.

Result

The insurer identified a company that fitted its criteria from the information we offered through both the briefing and ATLAS company information output. We were able to provide an introduction to the company, resulting in a successful partnership.

As a result of this, the insurer has been able to reduce underinsurance across its commercial property portfolio. With more accurate data, it was able to identify properties that were being undervalued and update premiums accordingly.

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