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Life through an underwriting lens: how does an insurance data model help underwriters with specialty risk?

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Published on:22nd January 2024
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Underwriting is a complex business that requires great skill. With new risks emerging all the time and hardening capacity in many lines, underwriters must work smarter and more efficiently than ever. They need to have full visibility over the policies and submissions on their books, no matter where they sit, and be able to quickly assess where their exposures lie. 

Policies themselves bring with them multiple layers of complexity. At the simplest level, one submission may need cover under several lines of business, and potentially each with its own endorsements. Or a property portfolio may be split across several different excess layers. It’s worth remembering that one submission can be used over multiple policies. Then we have the business of consolidating managed versus delegated authority business, bringing together a reconciling multiple claims or payment bordereaux from various places and cross-checking them against one another to make sure nothing is lost in the submission.

It’s enough to make even the smartest underwriters’ heads spin. 


Typically, policy admin systems don’t cater for submission level view of risk. Each layer has to be treated separately, which is not reflective of what an underwriter is trying to do, or how they look at business. They end up with multiple separate policies, with no easy way to tie them together without rekeying the same information and doing it again for another policy. Risky from a data quality point of view, and lacks that overarching insight that an underwriter really needs to understand the performance of their portfolio as a whole. 

It is for these reasons that modern underwriters are looking for systems that can handle the complexity of insurance submissions. And many are turning to low-code or no-code toolkits, believing that it will be easier to build the perfect system for themselves. They can be a helpful toolkit but they’re not good at modelling complex risk and it can be difficult to transfer low-code models from one business line to another.

For example, we recently expanded our lines of business to include ‘space’. Space insurance comes with a number of highly specific data points that need to be captured, but rather than having to build these for the customer from scratch, our insurance data model was able to be updated using the existing frameworks we’ve created. These aren’t generic fields that we try to apply to all lines of business, rather, considered models we’ve created based on our deep knowledge of how business is written, quickly tailored to get new products up and running quickly. Building something like that from scratch, with little or no coding experience would be out of the question for most busy underwriting businesses. 

What’s the solution?

Our team is committed to the underwriting community. It’s who we’ve built our business for. We have years of experience within the insurance industry, working alongside underwriters, so we know how they look at risk. We’ve spent years building an insurance data model that can handle complex lines of business without having to reinvent the wheel. Fundamentally, our underwriting platform is designed for the underwriter. This knowledge and expertise give our customers confidence that they haven’t got to do a lot of complex legwork in order to go live and implement new lines of business, our framework was built with them in mind using an underwriter perspective. 

Send Underwriting Workbench uses generative AI and automation to marry up the data from separate but related submissions into one view. Our data model deals with the problem of unstructured data and simply structures it. This operates as a single source of truth for the underwriter, who now only needs to go to one platform to interrogate all their data, no matter the source. All data is ingested, stored and managed in one place, this level of sophistication can only be achieved by people who know the industry inside out. 

Customers often ask whether we can handle this specialist line of business or that, but I say, ‘if you can explain it, we can handle it in our purpose-built underwriting workbench.’ 

Matt McGrillis, Send Co-Founder and CTO

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