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Underwriting judgment is one of the most valuable assets an insurer has, and one of the most fragile. Great underwriters develop a feel for risk that goes beyond guidelines, judgment built from thousands of decisions over a career. It's what shapes portfolio quality and shows up in loss ratios. But that judgment is unique to each individual underwriter. The underwriters around them don't have access to it, and when they retire or move on, it's gone. Meanwhile, the portfolio intelligence that should be informing every decision: how similar risks have performed, which broker relationships produce adverse selection, where losses concentrate—doesn't reach underwriters where and when they need it.

AI underwriting changes what’s possible here. Every submission processed leaves behind structured and unstructured traces that are sometimes not easily identified: risk factors extracted, reasoning paths, agent recommendations. When connected to the decisions and outcomes that follow, those traces form a learning loop, and Institutional Intelligence builds.

When this intelligence is properly put to use, your underwriting gets smarter. New underwriters ramp faster, operating on insights with expertise and portfolio context built in. Decisions become more consistent across the team, grounded in collective knowledge rather than individual memory. Expertise doesn't walk out the door when senior underwriters move on. The entire organization is upleveled with knowledge that used to live in silos.

What We’re Building

Strengthening Our Foundation

Sixfold's Underwriting Brain is at the center of everything we do. We’re deepening its expertise through specialty training and certification courses—building a sharper understanding of how underwriters work end-to-end and the nuances across lines of business. The Brain then captures carrier-specific guidelines and appetite with its patented appetite ingestion engine, refined over time through an underwriter feedback loop. Playbooks go further, encoding complex references, ontologies, and documented practices unique to your organization, so Sixfold doesn't just understand underwriting, it understands how you underwrite.

Building Institutional Intelligence

The Brain then builds Institutional Intelligence through a learning loop, the first phase of which is available today. Carriers can feed decision and outcome data back into Sixfold through enhanced case statuses and the Sixfold API. This data further shapes Sixfold’s understanding of your appetite and what wins business.

The learning loop expands as more data flows in. We're working closely with customers to build the data connections that matter most: broker performance, referral decisions, policy structure, losses. The goal is Institutional Intelligence that moves the metrics carriers actually manage to: winnability, portfolio performance, loss ratios. And because we deploy each customer in an isolated environment, the intelligence captured is yours alone.

Agentic self-learning comes next, identifying patterns across submissions, learning which signal combinations predict losses, the Brain self-refining its understanding of your desired portfolio over time. We're building toward self-learning deliberately, with the governance, performance monitoring, and controls enterprise insurers expect from Sixfold. 

Putting It To Work

A new agentic backend allows these layers of knowledge to be put to work. The Brain and the agents behind it build a research and assessment plan specific to each submission that comes in, reasoning through what information it needs and what actions it needs to take. Insights and recommendations are surfaced to underwriters, actions are taken, all grounded in the expertise and Institutional Intelligence that sits in the Brain. As Institutional Intelligence deepens, so does what Sixfold can do for your team.
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Current customers can begin feeding decision and outcome data into Sixfold today. Contact your Customer Success Manager to learn more.

Sixfold is actively conducting research sessions to help shape the future of our product. If you're interested in participating, reach out to us at hello@sixfold.ai.

Not yet a customer? Request a demo.

Yesterday, Sixfold hosted an event at Lloyd’s in London on the future of underwriting work.

The event started with a keynote from Dr. Naeema Pasha on the future of work across industries, along with findings from her research with Allianz. One of the key takeaways was that the future of insurance still needs to be focused on people. With AI becoming more present, the question is how to build trust, support empathy and skills development, and design roles for the next generation of talent.

“What stood out in my research with Allianz was how much people in the insurance industry care and are passionate about the industry and its future.“

─ Dr. Naeema Pasha, Researcher, Author and Writer

There was also a great question from an aspiring underwriter asking what skills are needed today. Soft skills came up a lot, things like curiosity and adaptability, but also something simple: get out there, engage with people in the industry, and ask questions.

At one point, the audience was asked if they would let a robot cut their hair. Most people wouldn’t. It’s a simple example, but it comes back to trust.

The panel discussion featured Simon Parris, CUO at Victor Insurance; John Enright, COO at Berkley; and Raoul Carlos, Founder & CEO at Torch Underwriting, and focused on whether this is the last generation of traditional underwriters.

They started by sharing where they are today when it comes to AI implementation. A clear theme was that adoption and engagement from underwriters really matters. Agentic AI is moving fast with a lot of potential, and there was a lot of discussion around how AI can improve broker relationships and risk assessments as a whole. Raoul talked about building AI into their foundation from day one, including the data layer needed to support institutional intelligence over time.

When it came to impact of AI, the conversation went beyond speed. Things like memorable engagement with brokers, pricing power through stronger relationships, more personal service, net promoter score, and the ability to think outside the box all came up. Better service and better products as well.

On hiring the future workforce, Raoul mentioned looking for curiosity and passion. Where the role used to be heavily focused on data wrangling, today it is much more about judgment, adaptability, creativity, and critical thinking. John highlighted integrity, trust, relationship skills, and the ability to work with a new toolkit. Simon mentioned market connections, strong underwriting fundamentals, and openness to new technology.

“The mechanics of how we work are changing, but this has happened hundreds of times throughout history.”

─ Raoul Carlos, Founder & CEO @Torch Underwriting

When asked what the underwriting role of the future might be called, the panel largely agreed it is still underwriting. Portfolio underwriting was mentioned, as well as next generation underwriter, but the core remains the same. From the audience, there were also questions around how to enter the industry. It is not just about academics. Apprenticeships matter. Continuous learning matters. And building teams with different perspectives and backgrounds still really matters.

On dealing with skepticism internally around AI, it was acknowledged that change can be uncomfortable. The advice was to make it part of the conversation, share examples, and host workshops. For insurance executives looking to learn more about AI, the message was to experiment and try it firsthand. Get into vibe coding, step outside of the comfort zone, and understand the opportunities by actually using the tools.

Lastly, Gianfilippo Giannini, Global Technical Coordinator Cyber Risk at Generali GC&C, took the stage for a Q&A and shared why they started looking for an AI vendor in the first place. They were operating in a soft market and needed to handle more submissions with the same headcount.

He also highlighted the importance of bringing underwriters in from the very beginning. The decision to choose Sixfold came down to security, privacy, a strong responsible AI framework, and a future proof roadmap, but also that the solution was clearly built for underwriting.

“Sixfold spoke the same language as us.”
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─ Gianfilippo Giannini, Global Technical Coordinator Cyber Risk @Generali GC&C

There was some early skepticism from users, but once underwriters saw how it supported their day to day work, feedback quickly became very positive. At one point, underwriters who were not part of the pilot started asking when they could start using Sixfold as well.

Looking ahead, the focus for Generali GC&C when it comes to AI in underwriting is on data driven underwriting, with better visibility into portfolio trends and broker success rates.

Most insurers have run an AI pilot. Far fewer have scaled one. At a recent Sixfold webinar, two carriers shared what it actually took to get there, and what production at scale really looks like.

Amy Nelsen, Head of UW Operations for US Middle Market at Zurich North America, and Jim Mormile, President of Professional Lines at Skyward Specialty, spoke about their experience rolling out Sixfold across their organizations.

Skyward is now live across more than 10 product lines with nearly 100 underwriters. Zurich has deployed across 30 US offices, with more than 200 underwriters using Sixfold in their daily workflow.

We walked through our five-step scaling framework with both of them to hear, in their own words, how each stage played out in practice.

Step  1: Pick Your Focus

The biggest mistake teams make is starting with AI and working backwards to find a problem. Both Amy and Jim were deliberate about doing the opposite, finding a genuine pain point first and then asking whether AI could solve it.

For Skyward, the problem was the triage wall. Underwriters were spending hours manually working through submissions just to determine basic appetite fit.

For Zurich it was starting with a use case which underwriters didn’t like doing: documentation. 

"We had a healthcare risk where the underwriter got about a 75-page submission. It wasn't until page 56 to 59 that they realized the submission should not be covered since it was out of appetite."
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─Jim Mormile @Skyward Specialty

Step 2: Prove It Works

Before scaling, you need a few signals that it's actually working, but that doesn't always come from a dashboard. Both Jim and Amy found that meaningful early indicators were user feedback.

For Jim, it was a veteran underwriter pulling him aside unprompted.

"The anecdotal evidence that really made us realize it was working: it was a veteran underwriter that came up to me and said, '[Sixfold] actually changed my day. It sped up my work progress and workflow'"
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─Jim Mormile @Skyward Specialty

The follow-up signal was equally telling “underwriters stopped asking whether they should use Sixfold and started asking when it was coming to their line of business.” That's when Skyward knew it was time to expand.

Step 3: Map Your Expansion

Scaling AI isn't a single rollout but it's a series of decisions about sequencing, readiness, and change management. Both organizations took a structured approach, but in different ways.

Skyward scored each of their 16 lines of business across criteria like guideline robustness, process standardization, underwriting complexity, and, critically, how tech-forward the underwriting team was. They then hand-picked early adopters rather than opening the floodgates.

Zurich took a geographic approach, piloting across four offices before expanding countrywide, and intentionally included underwriters at different levels of tech comfort so they could anticipate resistance before it became a problem at scale.

Both teams also addressed job security fears head-on.

"It's less about the underwriting role becoming irrelevant, and more about if you handle a $10M book of business versus a $20M book of business."
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─Amy Nelsen @Zurich North America

Jim's approach was to be explicit about what AI wouldn't do: no automated decision-making, no replacing the underwriter's final judgment. The framing was always about giving underwriters better information, not replacing their expertise.

Step 4: Roll Out in Phases

Both teams learned that trying to do too much too fast creates fatigue, and fatigue kills adoption. Skyward actually hit pause on one line of business after underwriters started showing signs of frustration. Rather than pushing through, leadership made the call to step back.

A few months later, that team came back ready to re-engage, pulled in by the FOMO of watching other lines of business benefit.

On the flip side, Skyward's phased approach led to real efficiency gains in deployment speed. Their first two lines of business took 12 to 14 weeks from introduction to production. By the time they were rolling out subsequent lines, they'd cut that down to eight weeks.

Amy's experience at Zurich echoed the same takeaway on speed:

"The most amazing part is how fast you can get from just a concept to deployment with Sixfold."
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─Amy Nelsen @Zurich North America

Step 5: Assess Impact

Once AI is live in production, measuring success means looking at two things in parallel: business outcomes and output quality. Neither alone tells the full story.

For Jim at Skyward, that meant tracking time to quote and number of quotes out the door, but also running a monthly accuracy review and a sentiment score across underwriting teams.

"We constantly look at both ends of the spectrum: the ROI metrics, and whether the accuracy is there to give our underwriters the information they need to make better decisions."
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─Jim Mormile @Skyward Specialty

Amy's approach at Zurich was similar, and revealing in its simplicity. Sixfold's success isn't measured separately from the business; it's measured through the business. When AI becomes embedded enough that you stop evaluating it as a standalone tool and start measuring it through your core business metrics, that's when you know it's really working.

"We're measuring our business outcomes like how many quotes underwriters are getting out the door and how much business have they bound. We also meet with Sixfold every month to look at quality metrics."
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─Amy Nelsen @Zurich North America

The Final Wisdom 

Scaling AI in underwriting isn't primarily a technology challenge, it's a people and process challenge. Both Amy and Jim closed with the same underlying message: be prepared for that thing change quickly in the world of AI, don't be afraid to fail, and don't stay stuck in proof-of-concept mode forever.

The insurers that will succed at scaling AI are the ones that move deliberately, learn fast, and bring their underwriters along for the ride from day one.

Watch the video recording of the entire webinar here.

In 2023, Brian, Jane, and I set out to build AI for underwriting. Not as another helper tool, but as the foundation for how underwriting should actually work going forward.

At the time, a few people thought we were crazy. But we believed underwriting was ready. And we believed underwriters deserved better.

That idea has now become real.

Today, I’m excited to share that Sixfold just raised a $30M Series B, led by Brewer Lane, with strategic investment from Guidewire, and continued support from Bessemer Venture Partners and Salesforce Ventures.

From Idea → The AI Underwriting Brain

Fast forward, we’ve built what we originally set out to build:

An underwriting AI brain that global carriers rely on every day.

Sixfold is already trusted by insurers like Zurich North America, Skyward Specialty, Guardian, Generali GC&C, AXIS, and Mosaic.

We’ve processed over 1 million submissions, across 40+ lines of business, supporting insurers representing $265B in gross written premium.

Sixfold is live across North America, the UK, Europe, Latin America, and Australia, underwriting risks across Property & Casualty and Life & Health. Production underwriting at global scale.

AI underwriting isn’t coming someday. It’s here, and Sixfold is leading it.

Underwriters Run the Book, Not the Tasks

We’ve already started to see the shift. Underwriters are no longer spending their days processing information. Instead, they’re making decisions. AI agents execute the work end-to-end. Humans focus on judgment, portfolio performance, and market opportunities.

Or put more simply:

Underwriting operations run on Sixfold. People run the strategy.

That’s the AI Underwriter.

Why Customers Love Sixfold

One of the biggest reasons Sixfold has scaled so quickly is that we don’t ask insurers to rip and replace everything.

Sixfold integrates directly into the tools underwriters already live in. Their workbenches, policy admin systems, existing workflows, and, of course, email inboxes.

Near-zero time to value isn’t a slogan. It’s how we’ve earned trust across the industry.

This Isn’t Theory — It’s Happening Now

What makes this moment exciting is that it’s no longer hypothetical.

Customers are already seeing major impact:

• Skyward deployed Sixfold across 11 underwriting teams and cut quote response time by 35%
• Zurich rolled Sixfold out to 200+ underwriters, saving up to 2 hours per submission

And the real win is what happens next:

• Underwriters are happier because they get time back
• Carriers respond faster with better quotes and better coverage
• Agents and brokers prefer working with those carriers
• Teams win more business and select risk more intelligently
• Competitive advantage starts showing up in combined ratio

Sixfold becomes the underwriting tool you can’t live without.

That’s what “AI transformation” looks like when it’s real.

Why Series B Now

We’ve proven autonomous AI works in real underwriting environments. Now it’s time to scale.

This funding lets us go faster on what matters most:

• More autonomous underwriting agents
• Deeper integrations into carrier workflows
• Portfolio-level visibility
• Global expansion across markets where demand is already there

Underwriting remains the most complex and important function in insurance.

We’re rebuilding it with AI at the core and underwriters in control.

Bring on even more #Funderwriting!

Think post was originally posted on LinkedIn

Over the past few years at Sixfold, we've helped more than 50 underwriting teams bring AI into their day-to-day work. From global carriers to innovative MGAs, teams ranging from five to 200 people across P&C, Life & Health, and specialty lines.

Different customers with different needs, but one common pattern: the teams that succeed with AI aren't the ones waiting for a perfect solution. They're the ones who start. They take the project out of the product roadmap and bring AI into how their team actually works, into the culture, the habits, the daily routine.

The goal is to take manual work off underwriters' plates so they have time to focus on complex decisions, bind more premium, and strengthen broker relationships. The secret is to simply start.

From our experience working with underwriters on a daily basis, one thing is clear: successful adoption doesn't come from trying to solve everything at once. It starts with one clear, impactful pain point.

Achieveing a successful start

1. ‍‍Pick a pain point that underwriters already feel every day. Something that eats up hours, like digging through pages of documents to find one relevant detail about a risk. When underwriters see real value, they'll use it and share the wins with their peers.
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2. Get leadership buy-in from leaders who understand why it matters, not leaders who add AI just to check a box. Leaders who actively support underwriters as they try something new make the difference.

And just as important: underwriters need to see what's in it for them. Concrete changes to their day-to-day work, not abstract efficiency gains.

“We focused on a use case underwriters actually asked for. And then accuracy became everything. If they can't trust the output, it doesn't matter how cool the technology is.”

─ Amy Nelsen, Head of UW Operations, Middle Market at Zurich North America

That's just the first step. We've put together a 5-stage guide that covers what comes next, from building trust, making it effortless to use, to spreading adoption across the team. 

Download the guide here

The bottleneck in life & health underwriting

Life & health underwriting is slowed by manual submission triage and the work of reading dense medical evidence for risk assessment. AI underwriting platforms now read and reason through an entire submission before an underwriter even opens it.

Every life and health submission arrives as a stack of unstructured information, like application data and medical records such as attending physician statements, lab results, and prescription histories, that can run to hundreds, even thousands of pages. Historically, underwriters spent the bulk of their time finding, reading, and organizing that evidence before they could make a single risk decision. That manual effort is the real constraint, not the decision-making process itself.

Sixfold changes that. Its AI agents read and reason across the full submission the way an experienced underwriter would, surfacing the relevant medical evidence aligned with the carrier's underwriting manual, flagging key risk factors, and delivering a clear health overview. All steps to support the underwriter's decision-making.

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Using medical software to read records and find key information faster

Medical evidence software exists because reading through the records is the most time-consuming part of a life & health case analysis. 

Sixfold processes attending physician statements, lab panels, and prescription data, extracts the clinically relevant findings, and ties them back to the risk factors that matter for the underwriting decision. Instead of skimming a 200-page record for a single relevant medical condition, the underwriter gets a structured, evidence-backed summary with a traceable source.

Because the system comes pre-trained with underwriting knowledge, it recognizes what matters most to a life or health risk from day one, rather than requiring months of configuration. It plugs into the underwriting workbench the team already uses, so the medical evidence summary appears within the existing workflow.

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Reducing time spent in manual submission triage with AI

Doing the initial review of a submission, checking if all the information is available to proceed or if it needs more data from brokers or agents, is where underwriting teams spend the most time on operational work. An AI platform automates triage by ingesting each submission as it arrives, assessing completeness, pulling and organizing the medical evidence, and producing a preliminary recommendation before a human review.

The result is that when underwriters start the case review, it's already prioritized, and the risk assessment is complete, ready for a final underwriting decision. Underwriters get to spend their time on judgment, complex risk cases, and broker relationships rather than on sorting and data entry. 

Across Sixfold customers, this contributes to a 50% reduction in turnaround time, hit ratios up 15% or more, and up to 30% more gross written premium per underwriter, because capacity is redirected from administrative triage to writing business.

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L&H Frequently Asked Questions

How do life and health insurers use medical-evidence software to speed up the underwriting process?

Life insurers use Sixfold's AI to automatically read and interpret medical records, attending physician statements, lab results, and prescription histories. It extracts relevant findings, links them to risk factors, and presents an evidence-based summary, so underwriters can focus on the decision rather than digging into information. It provides a risk assessment ready for decision-making, including recommendations on what to do next in a case.

Sixfold comes pre-trained with underwriting knowledge from day one. It integrates directly with existing underwriting workbenches, keeping the source evidence visible for every conclusion. Customers see processing times 50% to 97% faster as a result.

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How do underwriting teams reduce manual submission triage using AI platforms?

Underwriting teams reduce manual triage by having the AI platform ingest each submission on arrival, check completeness, organize the medical evidence, and automatically generate a risk assessment and next-step recommendations. Underwriters then start case review with cases that are already prioritized and pre-analyzed, focusing their time on risk judgment rather than sorting and data entry.

Having Sixfold help with triage, teams redirect capacity from administrative work to writing business. Sixfold customers report hit ratios up 15% or more and up to 30% more GWP per underwriter. Adoption stays high, over 90% of expected users, because the platform fits how underwriting actually works rather than forcing a new process.

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What are the top tools for automating life underwriting decisions?

The top tools for automating Life and Health underwriting decisions are purpose-built underwriting AI platforms that reason across a full submission, automated medical evidence and record summarization software, and integrations into the underwriting workbench. The strongest options combine all three: decisioning, evidence analysis, and workflow fit.

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About Sixfold

Sixfold is the first AI solution built specifically for insurance underwriting, in market since early 2023, and is live in production across 50 lines of business, including life & health lines. It has processed more than 1.5 million submissions globally and is used by more than 1,000 underwriters, from insurers like Guardian and New York Life.

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