Published on: 
January 17, 2024

It Takes You Months to Train AIs? You're Doing It Wrong

5 min read

It’s impossible in 2024 to be an insurance carrier and not also be an AI company. In this most data-focused of sectors, the winners will be the organizations making the best use of emerging AI tech to amplify capacity and improve accuracy.

This is a challenge and opportunity that Sixfold is uniquely suited to address thanks to our decades of collective industry and technological experience. We know insurers’ needs—intimately—and understand precisely how AI can overcome them. 

In previous posts, I’ve described how Sixfold uses state-of-the-art AI to ingest data from disparate sources, surface relevant information, and generate plain-language summarizations. Our platform, in effect, provides every underwriter with a virtual team of researchers and analysts who know exactly what’s needed to render a decision. But getting there is the rub. Training AI models (these “virtual teams”) to understand what information is relevant for specific product lines is no small task, but it’s where Sixfold excels.

To use AI is human, to create your own unique AI model is divine

Underwriting guidelines aren’t typically encapsulated in a single machine-readable document. They’re more likely to exist in an unordered web of internal documents and reflected in historic underwriting decisions. Distilling a diffuse cultural understanding into an AI model can take months using a traditional approach, but with Sixfold, it can be accomplished—and accomplished well—in as little as a few days.

Sixfold’s proprietary AI captures carriers’ unique risk appetite by ingesting a wide variety of inputs (be it a multi-hundred-page PDF of guidelines, a loose assortment of spreadsheets, or even past underwriting decisions) and translating it into an AI model that knows what information aligns with a positive risk signal, a negative one, or a disqualifying factor. 

With this virtual wisdom model in place, the platform can identify and ingest relevant data from submitted documents, supplement with information from public and third-party data sources, and generate semantic summaries of factors supporting its conclusions—all adhering to the carriers’ unique underwriting approach.

Frees human underwriters to do uniquely human tasks

It can take years for a human underwriter to master underwriting guidelines and rules, but that doesn’t mean human underwriters are no longer needed–quite the opposite. By offloading the administrative bulk to AI, underwriters can use their increased capacity to prioritize cases that align with their unique risk appetite. 

It can take years for a human underwriter to master underwriting guidelines and rules.

Consider a P&C carrier that prefers not to underwrite businesses that work with asbestos. When an application comes in, Sixfold’s platform processes all broker-submitted documents and supplements it with relevant data ingested from public and third-party data sources. If Sixfold were to then surface information about “assistance with obtaining asbestos abatement permits” from the applicant’s company website, it would automatically mark the finding as a negative risk signal (with clear sourcing and semantic explanation) in the underwriter-facing case dashboard. With Sixfold, underwriters can rapidly discern the applications that are incompatible with their underwriting criteria and quickly focus on cases aligned with their risk appetite.

Sixfold rapidly identifies disqualifying factors and frees underwriters to focus on applications aligned with their criteria.

Automating these previously resource-intensive data processing workflows allows carriers to obliterate traditional limits on underwriting capacity. The question for the industry has rapidly moved from “is automation possible?” to “how quickly can we get it implemented?” Sixfold’s purpose-built platform empowers customers to leapfrog competitors relying on traditional approaches to training AI underwriting models. We get you there faster–this is our superpower.

Users can peek behind the AI and review the positive and negative risk signals the system has been trained to look for.


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Alex Schmelkin
Co-founder & CEO, Sixfold