Remove Bottlenecks, Accelerate Decisions

Life & Health underwriters often have to go through hundreds of pages of medical documents to understand an applicant’s health profile, knowing that one missed detail could lead to the wrong coverage decision.

When we introduced our Life & Health Underwriting AI, we set out to give underwriters everything they need in one place: diagnoses, medications, and procedures pulled from applications and supporting documents, aligned to the insurer’s unique risk appetite. They no longer had to spend time manually going through medical documents. 

But after getting feedback from underwriters across global insurers, we realized something important: presenting information alone isn't enough. What they really need is the full health story, with all the pieces connected. For example, if a medication appears, they need to know the full context around it: how often it was prescribed and any related diagnoses or procedures.

That’s why we’re upgrading our Underwriting AI with Conditional Insights and Clinical Data, designed to reflect the way underwriters think about the overall health profile.

What’s New?

Conditions: From Medical Facts to the Full Story

Conditions completely change how Sixfold’s insights are presented to underwriters. 

Previously, all relevant data, like Personal Health, Medications, Procedures, and more, appeared as individual facts. While surfacing this information is essential, it didn’t show underwriters how it all connected.

The thing is, Life and Health underwriters don't analyze each fact in isolation; they think about how it fits into the bigger picture. What does this medication suggest? How severe is the condition? How does it all connect?

That’s why Sixfold now brings together all relevant information under a diagnosed condition, including:

• Medications and ongoing treatment
• Procedures and lab results
• The condition’s full history, with context and progression over time
  

For example, take Gastritis, an inflammation of the stomach lining that can cause pain, indigestion, and discomfort. An applicant might have been diagnosed with it a while ago, is taking medication such as Pantoprazole, and could have undergone procedures like Endoscopies five years ago.

Instead of treating these details as in isolation, Sixfold now recognizes they’re all part of the same conditions story. This mirrors exactly how an experienced underwriter would naturally connect the dots, seeing not just health facts, but the bigger picture of an applicant’s health history.

“This release has been a huge focus for our team, and the feedback from early users has been very positive. They’ve shared firsthand how impactful it is to have a single experience that brings together every aspect of an applicant’s medical history.” 

- Noah Grosshandler, Product Manager at Sixfold.

Want a closer look at how Conditions work? Join our live product demo on September 11th.

Core Clinical Data: Lab Insights and Historical Trends

Underwriters often have to go through dozens of lab results to answer: Does this applicant have an underlying condition? Are the results concerning? Could they worsen over time?

Core Clinical Data brings all of that information into one place, instantly. It pulls information found in lab results and medical records uploaded to Sixfold, presenting a pre-defined set of health indicators commonly tied to high-risk conditions such as: Vitals, Cardiovascular Health, and Hematology.

It gives underwriters instant and standardized insight: lab values, normal ranges, and historical progression, making it easier to assess the presence, progression, and severity of chronic conditions.

The impact? Core Clinical Data gives underwriters a snapshot of the applicant’s health at a glance, meaning time saved in reading lab reports that can be used to actually bind accounts. With immediate access to historical trends and key lab indicators, underwriters can make faster and more accurate decisions. 

How Guardian Cut Review Time in Half

Guardian, one of the largest life insurers in the U.S., faced a common challenge: manual case reviews slowed down underwriters and created bottlenecks in the underwriting process.

By adopting Sixfold for its Disability line, Guardian was able to automatically extract medical data, triage information faster, and speed up case assessments end-to-end.

The impact: a 50% reduction in review time, freeing underwriters from being stuck reviewing medical records and letting them focus on risk decisions. Read more about the results Guardian has seen here. 

Now, Guardian is expanding the program across more business lines, and you can see why! Join our Life & Health Product Demo on September 11th, where we’ll showcase how Sixfold works for life & health insurance underwriting. 

As AI becomes more embedded in the insurance underwriting process, carriers, vendors, and regulators share a growing responsibility to ensure these systems remain fair and unbiased.

At Sixfold, our dedication to building responsible AI means regularly exploring new and thoughtful ways to evaluate fairness.¹

We sat down with Elly Millican, Responsible AI & Regulatory Research Expert, and Noah Grosshandler, Product Lead on Sixfold's Life & Health team, to discuss how Sixfold is approaching fairness testing in a new way.

Fairness As AI Systems Advance

Fairness in insurance underwriting isn’t a new concern, but testing for it in AI systems that don’t make binary decisions is.

At Sixfold, our Underwriting AI for life and health insurers don’t approve or deny applicants. Instead, it analyzes complex medical records and surface relevant information based on each insurer's unique risk appetite. This allows underwriters to work much more efficiently and focus their time on risk assessment, not document review.

“We needed to develop new methodologies for fairness testing that reflect how Sixfold works.”
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— Elly Millican, Responsible AI & Regulatory Research Expert

While that’s a win for underwriters, it complicates fairness testing. When your AI produces qualitative outputs such as facts and summaries, rather than scores and decisions, most traditional fairness metrics won’t work. Testing for fairness in this context requires an alternative approach.
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“The academic work around fairness testing is very focused on traditional predictive models, however Sixfold is doing document analysis,” explains Millican. “We needed to develop new methodologies for fairness testing that reflect how Sixfold works.”

“The academic work around fairness testing is very focused on traditional predictive models, however Sixfold is doing document analysis,” explains Millican. “We needed to develop new methodologies for fairness testing that reflect how Sixfold works.”

“Even selecting which facts to pull and highlight from medical records in the first place comes with the opportunity to introduce bias. We believe it’s our responsibility to test for and mitigate that,” Grosshandler adds.

While regulations prohibit discrimination in underwriting, they rarely spell out how to measure fairness in systems like Sixfold’s. That ambiguity has opened the door for innovation, and for Sixfold to take initiative on shaping best practices and contributing to the regulatory conversation.

A New Testing Methodology

To address the challenge of fairness testing in a system with no binary outcomes, Sixfold is developing a methodology rooted in counterfactual fairness testing. The idea is simple: hold everything constant except for a single demographic attribute and see if and how the AI’s output changes.²

“Ultimately we want to validate that medically similar cases are treated the same when their demographic attributes differ,”
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— Noah Grosshandler, Product Manager @Sixfold

“We start with an ‘anchor’ case and create a ‘counterfactual twin’ who is identical in every way except for one detail, like race or gender. Then we run both through our pipeline to see if the medical information that’s presented in Sixfold varies in a notable or concerning way” Millican explains.

“Ultimately we want to validate that medically similar cases are treated the same when their demographic attributes differ,” Grosshandler states.

Proof-of-Concept

For the initial proof-of-concept, the team is focused on two key dimensions of Sixfold’s Life & Health pipeline.

1. Fact Extraction Consistency
Does Sixfold extract the same facts from medically identical underwriting case records that differ only in one protected attribute?

2. Summary Framing and Content Consistency
Does Sixfold produce diagnosis summaries with equivalent clinical content and emphasis for medically identical underwriting cases?

“It’s not just about missing or added facts, sometimes it’s a shift in tone or emphasis that could change how a case is perceived,” Millican explains. “We want to be sure that if demographic details are influencing outputs, it’s only when clinically appropriate. Otherwise, we risk surfacing irrelevant information that could skew decisions.”

Expanding the Scope

While the team’s current focus is on foundational fairness markers (race and gender), the methodology is designed to evolve. Future testing will likely explore proxy variables such as ZIP codes, names, and socioeconomic indicators, which might implicitly shape model behavior.

“We want to get into cases where the demographic signal isn’t explicit, but the model might still infer something. Names, locations, insurance types, all of these could serve as proxies that unintentionally influence outcomes,” Millican elaborates.

The team is also thinking ahead to version control for prompts and model updates, ensuring fairness testing keeps pace with an evolving AI stack.

“We’re trying to define what fairness means for a new kind of AI system,” explains Millican. “One that doesn’t give a single output, but shapes what people see, read, and decide.”

Sixfold isn’t just testing for fairness in isolation, it’s aiming to contribute to a broader conversation on how LLMs should be evaluated in high-stakes contexts like insurance, healthcare, finance, and more.

That’s why Sixfold is proactively bringing this work to the attention of regulatory bodies. By doing so, we hope to support ongoing standards development in the industry and help others build responsible and transparent AI systems.

“This work isn’t just about evaluating Sixfold, it’s about setting new standards for a new category of AI." Grosshandler concludes.

“This work isn’t just about evaluating Sixfold, it’s about setting new standards for a new category of AI. Regulators are still figuring this out, so we’re taking the opportunity to contribute to the conversation and help shape how fairness is monitored in systems like ours,” Grosshandler concludes.

Positive Regulatory Feedback

When we recently walked through our testing methodology and results with a group of regulators focused on AI and data, the feedback was both thoughtful and encouraging. They didn’t shy away from the complexity, but they clearly saw the value in what we’re doing.

“The fact that it’s hard shouldn’t be a reason not to try. What you’re doing makes sense... You’re scrutinizing something that matters.” said one senior policy advisor.

“The fact that it’s hard shouldn’t be a reason not to try. What you’re doing makes sense... You’re scrutinizing something that matters.”

— Senior Policy Advisor

One of the key themes that came up during the meeting was the unique nature of generative AI, and why it demands a different kind of oversight. As one senior actuary and behavioral data scientist put it: “Large language models are more qualitative than quantitative... A lot of technical folks don’t really get qualitative. They’re used to numbers. The more you can explain how you test the language for accuracy, the more attention it will get.”

That comment really resonated. It reflects the heart of our approach, we’re not just tracking metrics. We’re evaluating how language evolves, how facts can shift, and how risk is framed and communicated depending on the inputs.

The Road Ahead

Fairness in AI isn’t a fixed destination, it’s an ongoing commitment. Sixfold’s work in developing and refining fairness and bias testing methodologies reflects that mindset.

As more organizations turn to LLMs to analyze and interpret sensitive information, the need for thoughtful, domain-specific fairness methods will only grow. At Sixfold, we’re proud to be at the forefront of that work.

Footnotes

¹While internal reviews have not surfaced evidence of systemic bias, Sixfold is committed to continuous testing and transparency to ensure that remains the case as we expand and refine our AI systems.

²To ensure accuracy, cases involving medically relevant demographic traits, like pregnancy in a gender-flipped case, are filtered out. The methodology is designed to isolate unfair influence, not obscure legitimate medical distinctions.

‍Check Sources Instantly‍

Trust and transparency are essential when underwriters use AI in their daily work. Underwriters need to know that the information they rely on is accurate; otherwise, a policy decision could result in incorrect coverage, claims issues, or unnecessary risk for the carrier. 

One of the best ways to build that confidence is by clearly showing the source of each piece of information. That’s why we’re excited to introduce a new In-line Citations feature for our Life & Health customers. This feature makes it easy to check the source behind any insight Sixfold surfaces.

So, how does it work?

When reviewing a case in Sixfold,  underwriters can now see exactly where each fact came from, including the document and page number. Here’s what you’ll see when clicking into a fact card:

• Document category listed for each file.
• Page number shown on hover
• One-click access to the exact source page
• All of the documents where the fact was found

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Our goal? To increase underwriter confidence and efficiency by clearly showing the source of medical and lifestyle facts within the insurance application analysis.

New Info? Now Flagged for You

In Life and Health underwriting, it’s common for some cases to take time, sometimes weeks, to gather all the documents needed for final analysis. The result? A lot of new information is coming in, and it’s not always clear what’s actually new facts.

That’s where our new capability, New Case Facts comes in.

Now, when new facts are surfaced within a case, you’ll see a bell icon next to the relevant fact card, a simple way to flag which facts came from the latest documents added. You can click into the fact to see more context, including which document category it came from.

This makes it easier to understand what’s been added, without having to reread the whole submission. It’s especially useful when multiple underwriters are collaborating on a case; one might start the analysis, while a colleague might actually finish it.

With new facts clearly marked, everyone can stay aligned and quickly assess what’s different and what it means for the overall risk profile of the applicant.