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AI has now been implemented across most underwriting teams. Sixfold’s research report with insights from more than 500 underwriters and executives showed that 70% of teams now use AI in their daily workflows. 

So AI is officially part of how underwriting work gets done today. That means that every underwriting team is asking some version of the same question right now: what does a good working relationship with AI actually look like? Not how to turn it on, but how to work with it well, day to day, file by file. 

Like any skill, it has to be built. 

Why This Is a New Skill

Older automation tools did exactly what they were told.  Point them at a task and they run it. AI showing up in underwriting today is different. It can triage a submission, flag what is missing, and put together a recommendation with real reasoning behind it, without being walked through every step. That is closer to delegating to a colleague than operating a tool.

Delegating well is a skill. So is knowing when to double check someone's work, and knowing what you are still accountable for once you sign off. Underwriters already have this skill. They use it every time they train a new hire or rely on a broker's summary of a risk.

AI fluency is that same skill, applied to a new kind of colleague.

What AI Fluency Actually Means

Anthropic defines AI fluency as the ability to work with AI systems in ways that are effective, efficient, ethical, and safe. Applied to underwriting, that becomes something more specific: knowing what to hand to an AI colleague and what to keep for yourself, how to direct it well, how to tell if its work holds up, and taking real accountability for what to sign off on.

A good underwriting AI solution should be easy to pick up on day one. Fluency is not about learning the tool. It is the expertise an underwriter builds working alongside it over time, the same way a new analyst gets sharper the longer they work with a good manager.

Adoption Is Not the Same as Fluency

Most underwriting leaders are already tracking adoption: how many underwriters log in, how many submissions run through AI, how many hours it touches in a week. Those numbers matter, but they measure something different from fluency.

Adoption asks whether the team is using it. Fluency asks whether the team is using it well. Usage is easy to measure and easy to report. Fluency takes watching how people work, not just how often they log in.

The Four Levels of AI Fluency

Underwriting teams tend to land somewhere on a spectrum of these levels. 

Resistant. Uses AI only when required, defaults back to the old process whenever there is a choice. Keeps AI at arm's length.

Compliant. Follows instructions, nothing more.

Experimenter. Curious. Pushes it to see what it can do.

Fluent. Treats AI like a team member: directs it, corrects it when it is wrong, and lets it take on more of the file as it proves itself. 

The goal is not to force everyone to the top overnight. It is to give people a clear path from wherever they are now to fluent.

The Four Skills of an AI Fluent Underwriter

Fluency breaks down into four concrete skills. 

• Two of them, delegation and direction, your team has probably already set some defaults for.
• Two of them, discernment and diligence, are yours alone, file by file, no matter what your team has decided above you.

1. Delegation: Knowing What to Hand Off

Your team has probably already drawn this line somewhere, even if no one wrote it down. A rough order, from safest to hand over to hardest:

Almost always safe to delegate: document extraction and synthesis, initial submission triage, appetite fit scoring against stated guidelines.

Increasingly common, worth considering: declining submissions that are clearly outside appetite, drafting follow up questions for a broker, flagging missing information before a human review.

Higher stakes, decide deliberately: quoting the best fit submissions with no human touch, handling an entire line of business end to end.

Usually keep human: real ambiguity about intent or the relationship at stake, a risk with no real precedent, final sign off.

That line moves as trust builds, and it is worth knowing where your team currently draws it, not just accepting it as fixed. The harder question is what you do with the file that sits right at the edge of it. Is the answer to proceed or escalate, and is that written down anywhere, or are you working it out on instinct at the moment?

2. Direction: Knowing How to Direct It Well

Getting a good result from AI depends on the context you give it, the same way a sharp new analyst does better work when the brief is specific instead of vague. Vague direction gets a vague draft back. A specific brief, with the appetite guideline, the format expected, and what "good" looks like for that line of business, gets something an underwriter can actually use.

Your team has likely already set some of this for you: appetite guidelines, expected output format, a shared definition of "good" by line of business. That part is not yours to reinvent file by file. What is yours is the context the defaults do not cover. On an unusual account, do you add the known edge case, the past miss on a similar risk, the thing worth flagging that no default would catch on its own? That is the difference between running the tool as configured and actually directing it.

3. Discernment: Knowing How to Judge Its Work

This is where calibrated trust comes in, and it is almost entirely a file by file skill. It means knowing which outputs need real scrutiny and which do not, and checking the reasoning behind a recommendation, not just whether the final number looks right.

Scrutiny should scale with what is actually at stake, not stay flat across every file:

-Routine renewal, within appetite, standard limits: a light check is enough.

- New business, within appetite, standard complexity: a moderate check.

- Outside appetite, an unusual class, large limits, or no real precedent: full scrutiny, led by a human.

Six in ten executives say the main reason AI projects never reach production is that outputs were not accurate or reliable enough to act on. You are the one in a position to catch that before it becomes a company wide problem. That is exactly the gap discernment is meant to close, catching where the reasoning does not hold up before it becomes a decision, not waving everything through.

Your team may already have these tiers written down somewhere. If they do, check your own recent submissions against them: were the last ten files you reviewed given attention based on what was actually at stake, or did they all get roughly the same amount regardless of risk? If your team has not written the tiers down yet, that gap is worth raising, since it is the difference between calibrated trust and just going with your gut.

4. Diligence: Owning the Outcome

An underwriter has always been accountable for their file, no matter what informed the decision: a broker's summary, a colleague's read, or an AI recommendation. AI does not change that standard.

Your team should have a written standard for when an AI's role in a file needs to be noted internally versus disclosed to a broker, and a minimum bar for what gets documented for audit purposes. If that standard exists, follow it. If it does not, do not wait for someone else to write it. A short note on what the AI flagged and how you weighed it is the kind of thing you will want on file six months from now regardless, and it costs you a sentence to add now.

One Submission, Start to Finish

Here is what all four skills look like on a single file.

A medium sized commercial account comes in through the usual intake channel. Sixfold reads the submission, extracts the key details, and flags two things: a coverage request that sits at the edge of the team's stated appetite, and a prior loss that was not called out in the broker's summary.

Delegation: the underwriter already knows triage and extraction are AI's job on every file like this. The appetite edge case is not, so it gets flagged for a human look rather than auto processed.

Direction: the underwriter adds context the AI would not have on its own, a note that this broker's book has run clean for the past three years, which changes how much weight the prior loss should carry.

Discernment: because this file sits at the edge of appetite, it gets a full review rather than a light check. The underwriter reads the reasoning behind Sixfold’s next step recommendation, not just the recommendation itself, and confirms it holds up against the added context.

Diligence: the underwriter signs off on a quote, with a short note in the file recording that AI flagged the prior loss and the underwriter weighed it against broker history before deciding. That note is what makes the file clean six months from now if anyone asks how the decision was made.

Building Your AI Fluency

If you want to hear how others are working through this on their own files, Sixfold's upcoming webinar brings together underwriters that are using AI in their daily life.

Register here to join us together with underwriters from Generali GC&C and Mosaic Insurance on the 8th of September or watch the recording on-demand.

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FAQs

What is AI fluency in underwriting?
AI fluency is the skill of working effectively with AI in day to day underwriting work. It means knowing what to hand off to AI, how to direct it well, how to judge its output, and taking full accountability for the final decision.

How is AI fluency different from AI adoption?
Adoption measures whether a team is using AI, tracked through logins, submissions processed, or hours saved. Fluency measures whether the team is using it well. A team can have high adoption and still be far from fluent.

What are the four levels of AI fluency?
Resistant, Compliant, Experimenter, and Fluent. Most teams sit somewhere on that spectrum rather than at one end, and the goal is steady progress toward Fluent rather than an overnight jump.

What are the four skills of an AI fluent underwriter?
Delegation, direction, discernment, and diligence. Delegation and direction are usually set at the team level. Discernment and diligence are decisions each underwriter makes file by file.

How much scrutiny should an underwriter apply to AI recommendations?
Scrutiny should scale with what is at stake. A light check works for routine, in appetite renewals. Full human led scrutiny applies to anything outside appetite, unusually complex, or without real precedent.

Does using AI change what an underwriter is accountable for?
No. An underwriter is accountable for the file regardless of what informed the decision, whether that is a broker's summary, a colleague's input, or an AI recommendation. AI does not lower that bar.

Why does documentation matter when AI is involved in a file?
A short note on what AI flagged and how the underwriter weighed it protects the file if anyone reviews the decision later. Teams that skip this step are relying on memory instead of a record.

Sixfold's AI Underwriter is now available in Salesforce via PS Advisory's custom MuleSoft connector, helping carriers move submissions through underwriting faster and more consistently.

Carriers should not have to leave the systems their underwriters already use to get value from AI. That is the idea behind Sixfold's new partnership with PS Advisory, an insurance-focused Salesforce and AI execution partner. The partnership embeds Sixfold's underwriting intelligence directly into the Salesforce workflows carriers already run.

Why PS Advisory

PS Advisory works exclusively with the insurance industry, supporting carriers, MGAs, brokers, and reinsurers with Salesforce solutions spanning distribution, underwriting, policy, and claims. The company has built a custom MuleSoft connector that provides bidirectional connectivity between Sixfold and Salesforce, letting submissions, enrichment data, and underwriting decisions move automatically between systems.

"PS Advisory combines deep insurance knowledge with Salesforce expertise, so they understand both how underwriters work and the technology they use," said Roger Ferrandis, Head of Partnerships at Sixfold. "That's what makes it possible to bring Sixfold's intelligence directly into those workflows, letting carriers move faster without having to change how they already work."

What Changes for Insurers

Carriers already using MuleSoft can extend their current setup with the Sixfold connector, while others can implement MuleSoft as part of the integration, without replacing their existing technology. The connector helps Sixfold automate submission intake, standardizes data, identifies missing information, and simplifies risk assessment against a carrier's own appetite and portfolio, reducing manual rekeying and avoiding lengthy custom integration projects.

"Our MuleSoft connector links Sixfold and Salesforce directly, giving carriers and MGAs a practical way to bring AI into existing underwriting workflows securely and at scale," said Andrew Bartels, CEO, PS Advisory. "They can build on technology they've already invested in and reduce operational friction."

Together, Sixfold and PS Advisory aim to make AI underwriting a practical part of the Salesforce workflows carriers use every day.

How to Get Started

Curious to learn more about the PS Advisory partnership or how Sixfold can support your underwriting team? Get in touch with our team. 

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Frequently asked questions

What is the Sixfold and PS Advisory partnership?
Sixfold and PS Advisory have partnered to bring Sixfold's AI Underwriter directly into Salesforce, using a custom MuleSoft connector PS Advisory built for bidirectional connectivity between the two systems.

What does PS Advisory do?

PS Advisory is a consulting firm focused on the insurance industry, helping carriers, MGAs, brokers, and reinsurers get more value from Salesforce. It designs and implements Salesforce solutions across distribution, underwriting, policy, and claims, and has extensive MuleSoft integration expertise.

Do carriers need to replace their existing systems to use this integration?

Carriers already using MuleSoft can extend their setup with the Sixfold connector, and others can implement MuleSoft as part of the integration. Either way, carriers keep their existing technology rather than replacing it.

What results have Sixfold customers seen?

Sixfold has processed more than 1.5 million submissions across more than 50 lines of business for P&C and L&H carriers, MGAs, and reinsurers. Customers have seen processing times improve by 50% to 97%, hit ratios increase by 15% or more, and gross written premium per underwriter rise by up to 30%.

Does the integration work with existing Salesforce environments?

Yes, PS Advisory offers prebuilt and tailored integrations so Sixfold's capabilities can be deployed directly within the Salesforce environments underwriters already use.

Insurers should not have to replace the systems their underwriters already use to get value from AI. That is the premise behind Sixfold's new partnership with Sollers Consulting, the international insurance technology consultancy.

The partnership combines Sixfold's underwriting AI solution with Sollers' insurance consulting and implementation expertise, so insurers can modernize underwriting faster. Sixfold and Sollers are already collaborating on several engagements across Europe, the UK and North America, each one tailored to the insurer's technology and underwriting requirements.

Why Sollers

Sollers Consulting has worked with more than 150 insurance organizations worldwide, including Admiral, Aviva, AXA, Beazley, IAG, Liberty, QBE, Sompo, Tokio Marine and Zurich. Founded in 2000, Sollers works from 16 offices across Europe, North America and Asia Pacific, with expertise spanning underwriting transformation, core insurance systems, cloud technology and systems integration.

Sixfold integrates with the technology insurers already run, and Sollers knows how that technology is configured and used at a given carrier. The two companies also share strategic technology partners, including Guidewire and Salesforce, so insurers can deploy AI inside their existing ecosystems.

"Insurers should not have to replace the systems their underwriters already use to get value from AI. Sixfold works within those systems and comes with underwriting knowledge built in from day one. Sollers knows how those systems are set up and used. Together, that gets insurers to better underwriting results faster."
- Roger Ferrandis, Head of Partnerships at Sixfold

What Changes for Insurers

Insurers get AI, consulting, and technology implementation within a single transformation program rather than coordinating a software vendor and an integrator separately.

For underwriting teams, that means submission intake is automated, risk is assessed against the carrier's own appetite, and underwriters get transparent, explainable recommendations, all within their existing systems and workflows.

"We've built Sollers' underwriting practice around the belief that the right technology, applied to the right part of the process, delivers outstanding results. Sixfold makes submission handling faster, more consistent and more intelligent. Together, we can help insurers achieve measurable results quickly while laying the foundations for underwriting transformation."
- Jakub Śliwiński, Head of Underwriting at Sollers

The two companies will also work together on joint educational initiatives, including webinars and industry engagement for carriers and MGAs.

How to Get Started

To learn more about the partnership or how Sixfold can support your underwriting team? Get in touch with our team. 

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Frequently asked questions

What is the Sixfold and Sollers Consulting partnership?
Sixfold and Sollers Consulting have formed a strategic partnership that combines Sixfold's underwriting AI solution with Sollers' insurance consulting and implementation expertise. The two companies work together on insurer transformation programs across Europe, the UK and North America.

What does Sollers Consulting do?
Sollers Consulting is an international insurance technology consultancy. Founded in 2000, it works from 16 offices across Europe, North America and Asia Pacific on underwriting transformation, core insurance systems, pricing, policy administration and cloud technology. It has worked with more than 150 insurance organizations worldwide.

Do insurers have to replace their underwriting systems to use Sixfold?
Sixfold integrates with existing technology rather than replacing existing underwriting systems. Sollers' role in the partnership is knowing how a given carrier's systems are set up and used, so Sixfold can be deployed into that environment.

Which regions does the partnership cover?
Europe, the UK and North America. Sixfold and Sollers are already collaborating on several insurer engagements in those markets.

What results do insurers see with Sixfold?
Sixfold customers see processing 50% to 97% faster, hit ratios up 15% or more, and up to 30% more GWP per underwriter. Sixfold has processed more than 1.5 million submissions across more than 50 lines of business for P&C and L&H insurers, MGAs and reinsurers.

Does the partnership support Guidewire and Salesforce?
Sixfold and Sollers share strategic technology partners including Guidewire and Salesforce, so insurers can deploy AI within their existing ecosystems.

Is the partnership relevant to MGAs as well as carriers?
Sixfold works with P&C and L&H insurers, MGAs and reinsurers, and the two companies are planning joint webinars and industry engagement for both carriers and MGAs.

In June we launched the AI Underwriter, an agentic system capable of producing real underwriting work product. It reasons through risk evaluation and arrives at recommendations and next steps, approaching underwriting the same way an underwriter would. One of our early testers described it like working with a peer, and we love that perspective.

Building a fully agentic system for underwriting is complex. It requires reasoning across context that changes with every file: how a submission fits a book, what a broker's history signals, where a risk sits relative to appetite. Understanding that deeply influenced how we built the AI Underwriter. It's why we went fully agentic, why governance was built into the architecture from day one, and why we invested heavily in evaluation infrastructure to make sure the system behaves correctly across every line of business.

What we had to build

Underwriting submissions never arrive in perfect condition. 

A property submission might include a schedule of values with a zip code transposition, a loss run that looks clean but covers a period that doesn't tell the whole story, and a broker application where the occupancy description doesn't match what's publicly known about the risk. Before our system could reason about a submission, it had to read it across every document type, in the form it actually arrives.

We built data infrastructure specifically for this: pipelines designed for the artifacts underwriters actually work with: SOVs, loss runs, medical evidence, broker applications, third-party and public data sources. That work alone, document processing, governance, security, integration across data sources, and the resource management that makes large language models run reliably at scale, was a significant engineering undertaking before we wrote a single line of underwriting logic.

A system that is capable but ungovernable is not deployable.

Governance was never an afterthought. Every recommendation the AI Underwriter produces has to be traceable from the output back through the reasoning to the guideline interpretation that drove it. The compliance team has to be able to explain it. The regulator has to be able to audit it. A system that is capable but ungovernable is not deployable.

And we wanted a system that could underwrite right out of the box. Not after weeks or months of additional configuration and training. A system that understands not just what good underwriting looks like, but what bad underwriting looks like, and that the answer is different by line of business, by carrier, by team. A workers comp underwriter and a property underwriter are reading the world differently. The red flags are different. The tolerance for ambiguity is different. Building evaluation frameworks that validate correct behavior across all of these contexts requires a testing discipline that’s taken years to build and refine.

And we wanted a system that could underwrite right out of the box. Not after weeks or months of additional configuration and training.

Here’s Shirley Shen, Lead Product Manager, and Ryan Garver, Staff Software Engineer, in their own words on what went into building this:

How the agent works

One of the things that took us the longest to get right is something that sounds simple: knowing what kind of work you're doing at any given moment.

The AI Underwriter sequences every step of the underwriting workflow itself, adapting as it goes. At every step, for every line of business and every submission, it sorts the work correctly. Some work is mechanical: pulling data, checking it against guidelines. Some work requires judgment: reasoning through how an exposure fits a book of business. Some work rises to the level of a recommendation, where the agent escalates to a human with a point of view. And some work is never the agent's call, full stop.

The AI Underwriter sequences every step of the underwriting workflow itself, adapting as it goes. At every step, for every line of business and every submission, it sorts the work correctly.

That sorting isn't static. Underwriting a named entity, a schedule of properties, and a list of assets are genuinely different problems. Property, cyber, and workers' comp each define risk differently. What's mechanical in one context is a judgment call or an escalation in another.

Getting that right is what separates an AI Underwriter from an AI that assists underwriting. It's also what makes it governable.

Why agentic mattered

Underwriting is not a checklist. A good underwriter reads a submission, forms a hypothesis, looks for what would change their mind, adjusts based on what they find, and arrives at a judgment. It is a long-horizon reasoning task, dynamic, iterative, and sensitive to context. Taking an agentic approach allowed us to build a system that models how underwriting actually happens.

Underwriting is not a checklist. A good underwriter reads a submission, forms a hypothesis, looks for what would change their mind, adjusts based on what they find, and arrives at a judgment. It is a long-horizon reasoning task, dynamic, iterative, and sensitive to context.

Early usage has shown just how valuable that is. For example, an underwriter had a submission come in with a mistyped zip code. We hadn't written an instruction for that scenario. The agent noticed the value didn't reconcile with the rest of the submission, did its own research to identify the correct location, and used that corrected geography in the risk assessment. Nobody gave it that path. It found it on its own, because it was reasoning toward a goal within the context of good underwriting.

What you can't shortcut

The question we often get is: why can't we just build this ourselves? Here's our honest answer.

Yes, it's buildable. We are proof of that. But here is what it actually takes. 

World-class AI engineers who do nothing but this. Evaluating every new model as it comes out, determining the right model for the right task, building evaluation frameworks specific to underwriting, and doing it again and again.

Domain expertise deep enough to know what correct behavior looks like in the first place.

Governance infrastructure that satisfies model risk requirements. 

The durability stakes are high. AI-powered technology depreciates fast. The models underneath change often, sometimes multiple times in a month. The patterns get superseded. For a carrier whose core business is insurance rather than AI research, that ongoing investment can be hard to justify.

This is all we do. We’ve processed more than 1.5 million submissions across 50+ lines of business. That experience has improved how we build: the edge cases we know to test for, the failure modes we've already solved, the LOB-specific nuances baked into our evaluation frameworks.

What this means for underwriters

Even the best underwriters are limited by working memory, bandwidth, and the boundary of their own desk. They can't hold an entire book of business in their head while evaluating a submission. They can't recall every broker pattern, every prior decision, every appetite shift. They learn mostly from their own cases, rarely from a colleague's.

The AI Underwriter gives underwriters what they don't have today: the carrier's full institutional memory at the moment of decision, on every file. It surfaces what is missing instead of summarizing what is there.

The AI Underwriter gives underwriters what they don't have today: the carrier's full institutional memory at the moment of decision, on every file. It surfaces what is missing instead of summarizing what is there. It brings in the broker history, the portfolio context, the prior decisions, the recommended next action. The underwriter makes the call. The AI Underwriter makes sure they are making it with everything they should know.

Reach out to us at hello@sixfold.ai to see how the AI Underwriter can work for your underwriting.

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If you're a Chief Underwriting Officer, Innovation Lead, Head of IT, or have a similar role in the Life & Health underwriting space, you've probably come across all types of AI and tech solutions that claim to streamline life and health underwriting workflows. It can be overwhelming to understand the differences, what each solution actually does, how they help underwriters review cases more effectively, and whether they actually reduce mortality slippage and improve placement rates.

Most of them reduce the manual work involved in reviewing lengthy medical records and assessing cases, but they do it in different ways, addressing different parts of the underwriting process. For example, a solution built to increase straight-through processing rates of standard cases won't solve the same problem as one built to help underwriters assess table-rated cases faster.

Knowing which problem you're actually trying to solve is what determines which solution is right. Is it automating decision-making for standard risks? Is it getting a structured summary of all medical records? Or is it having risk insights and recommendations aligned to your underwriting guidelines?

This guide breaks down the main categories to help you understand what the best fit for you is.

When the Manual Approach Stops Scaling

Most Life & Health underwriting teams are facing some version of the same two core challenges. That’s what’s often driving the push toward new technology, even if those challenges might show up differently depending on team size or the mix of cases they underwrite.

• Capacity: There's a consistently high volume of applications that gets more intense during busy seasons, and not enough underwriters to handle them as quickly as needed. The capacity issues get compounded by an aging workforce, which means difficulties in hiring experienced underwriters. Straightforward cases sit in the same queue as complex ones, and turnaround times slow down. Failing to keep up can impact placement rates, giving other insurers the chance to get there first.‍
• Quality: The cases that actually reach underwriters take too long to assess, or two underwriters on the same case can reach different conclusions. Sometimes both. Slow decisions affect your bottom line. Inconsistent ones lead to mortality slippage and erode the value of your guidelines over time.

These are related problems, but they need different solutions: a tool that reduces the number of cases reaching your team does very little for the quality of decisions on the ones that do. A tool that helps underwriters read faster often doesn't make their decisions more consistent. ‍

The Solutions Landscape‍

Here are the five main types of underwriting technology insurers use today to solve these problems.

Accelerated Underwriting Systems

Description: Accelerated Underwriting (AU) systems are designed to maximize automated decision rates, allowing eligible applicants to get approved without requiring traditional medical exams and lab tests. Both large reinsurers and third-party technology vendors offer AU solutions that carriers can adopt and configure to their needs.

These systems handle straightforward cases quickly and consistently, and most insurers today already use this type of technology. But there are still many cases that are too complex for these solutions, whether it's because of a higher-risk diagnosis, the coverage amount needed, a medication flag, or missing information, and that require an underwriter's review. That's where AU reaches its limits.

With these systems, underwriters still have to assess complex cases manually, reviewing hundreds of pages of APS, EHRs, and lab results to understand the patient's clinical history.

Type of technology: Rules-based engine, often combined with predictive models and machine learning.

Best for: Insurers looking to increase the volume of applications that get decided automatically at point of sale, without manual underwriter involvement. Helps increase Straight Through Process rates, reduce turnaround time, and free up underwriters from reviewing less complex cases.

Watch out for: AU systems don't solve the complex case problem. The cases that get referred out still need to be reviewed by a human underwriter, and that process remains largely manual without additional tooling. With 41% of cases going straight to traditional underwriting and 88% of cases requiring some level of human review, that's a significant share that AU simply can't handle.¹

Reinsurer-Built Underwriting AI Assistants

These tools are helpful if you're looking for an out-of-the-box summarization solution. The main thing to keep in mind is that they're typically anchored to the reinsurer's own guidelines, so customization options vary. They also often come bundled with the reinsurer's broader platform and services, so adopting one can mean a deeper commitment than just the tool itself.

With these systems, underwriters often still have to assess non-medical risks separately, manually leverage risk calculators, and return to the original APS or EHRs to fill in gaps.

Type of technology: OCR, NLP, and LLMsS for document ingestion and summarization, with some tools also incorporating structured decision logic for risk guidance.

Best for: Insurers who want a ready-to-use tool to help underwriters navigate referred cases faster, with medical information organized and presented in a structured, easy-to-read dashboard

Watch out for: These tools are typically built around the reinsurer's own guidelines, so customization options may vary. Adopting them often means signing up for the reinsurer's broader platform and services, which creates a level of vendor lock-in worth factoring in before making a decision.

Medical Record Summarization Tools

Beyond reinsurer-built tools, there are vendors focused specifically on medical record review. Not built exclusively for underwriting, these solutions are also used in claims, clinical, and legal workflows. They use AI to summarize and organize information from medical records, giving underwriters a more user-friendly view of an applicant's health history in one place.

Although they're good at giving an overall medical summary, all the information is surfaced regardless of relevance, which can still result in information overload. Underwriters still have to sift through often lengthy summaries to find what actually matters for their decision. These tools also aren't configured to a carrier's specific underwriting guidelines or appetite, so the insights they surface reflect a general medical view rather than what matters most to your team.

Type of technology: ML, NLP, LLMs, and AI agents, backed by a proprietary Medical Knowledge Base

Best for: Insurers looking for a standalone tool to help underwriters process medical records faster, without needing it to align with their specific underwriting guidelines

Watch out for: The information is comprehensive but not prioritized against your guidelines, and summaries can still be lengthy, so underwriters still have to figure out what's actually relevant to each case.

LLMs

Tools like ChatGPT or Claude are flexible, easy to use, and can analyze a wide range of documentation, including medical records. They're great for quick searches, general summarization, and easy enough to get started with so that an underwriter could set up a basic underwriting skill themselves.

The challenge is that they weren't built specifically for underwriting. Tailoring them for Life & Health underwriting requires significant time, money, and effort, and they may still not meet the explainability and auditability requirements that regulators increasingly expect.

Type of technology: LLMs

Best for: Quick searches, general summarization, or early-stage experimentation before committing to a purpose-built solution

Watch out for: Because they're not built for underwriting, outputs won't be as consistent or accurate as a purpose-built solution. Without significant customization, these tools won't align with your underwriting guidelines and won't be specifically designed to meet the regulatory and data protection standards required for use in production, increasing the risk of compliance issues.

Underwriting AI (Sixfold fits in here)

Sixfold generates risk insights and recommendations to support your team's decision-making, working directly within the systems you already use. It's designed to handle the full underwriting analysis of cases that reach your underwriters, from triage to deep review, including the most complex cases.

Rather than just summarizing information, Sixfold reasons across all relevant documents and surfaces the details your underwriters need, aligned to your specific guidelines. It works like a colleague that knows your book: tracking decisions over time, applying what it learns, and recommending best next steps. Keeping underwriters in control to agree or disagree with any assessment.

It can process any type of document, including messy, unstructured data, so there's no need for separate tools to extract and organize data first.

Type of technology: LLMs, AI agents, guideline-aligned risk engine

Best for: Insurers looking for underwriting AI that goes beyond summarization: improving speed without compromising underwriting quality. Aligns with your specific guidelines across the full underwriting workflow, with proven results (e.g. 50% efficiency gains for Guardian) and built to be compliant and fair for insurance underwriting from day one.

Watch out for: It's built for insurers who want AI that actually reasons and understands risk, not just summarizes information.‍

Side-by-Side Comparison

Below is a simple table showcasing the differences.

We hope this guide helped you better understand the different solutions in the market.

If Sixfold sounds like it could be the right fit for your team, reach out for a demo to see what it looks like in practice. 

‍Learn more about Sixfold’s Life & Health product here.

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FAQ 

What is the difference between accelerated underwriting and automated underwriting in life insurance?
Accelerated underwriting (AU) is a Life & Health underwriting approach that allows eligible applicants to get approved faster, without requiring traditional medical exams and lab tests. It does this by using external data sources like electronic health records, prescription history, and MIB records to assess risk. Automated underwriting is a broader term referring to any rules-based system that makes decisions without manual underwriter involvement. In Life & Health, AU is the more precise term. According to Gen Re's 2025 survey, around 59% of individual life applications qualify for an accelerated underwriting path.

How long does it take to get value from an AI underwriting solution in L&H?
Purpose-built AI underwriting solutions are designed to deliver value faster than traditional technology implementations. Unlike legacy core system replacements that can take months or years, they integrate with existing systems and workflows, meaning carriers can reach production without lengthy implementation projects. The key variables are guideline configuration, data readiness, and how much the solution needs to be customized to the carrier's specific appetite and workflows.

What percentage of life and health cases still require human review despite automation?
According to Gen Re's 2025 U.S. Individual Life Next Gen Underwriting Survey of 30 carriers, 41% of applications were processed through a traditional underwriting path, 47% were eligible for accelerated underwriting, and only 12% qualified for fully automated decisioning. This means the vast majority of case volume still lands on underwriters' desks, making the quality of the manual review process as important as the automation layer above it.

What is mortality slippage in life insurance underwriting?
Mortality slippage is the financial loss that occurs when underwriters approve risks at better rate classes than their actual mortality profile warrants. It typically results from inconsistent decision-making across underwriters reviewing similar cases. According to Gen Re's 2025 Next Gen Analytics study, overall AU slippage improved to 12.3% in 2025 after several years of upward pressure. Over half of carriers estimate their mortality slippage at between 6% and 15%.

How do AI underwriting tools handle data protection in Life & Health?
Data protection standards vary significantly across solution types. General-purpose LLMs were not built with insurance-specific security or regulatory requirements in mind, making them unsuitable for production use without significant customization. Purpose-built underwriting AI solutions are designed to meet the data governance, auditability, and compliance standards that Life & Health regulators expect, including requirements under the EU AI Act and the NAIC Model Bulletin on AI Systems, which continues to be adopted by U.S. states.

What is vendor lock-in risk when adopting a reinsurer-built underwriting tool?
Vendor lock-in occurs when adopting a reinsurer's AI tool requires signing up for their broader platform and services, making it difficult to switch providers later without disrupting workflows. Reinsurer-built tools are anchored to the reinsurer's own guidelines and ecosystem, meaning the carrier's underwriting process becomes tied to that reinsurer relationship beyond just the tool itself.

How do placement rates connect to underwriting cycle times in life insurance?
Faster underwriting decisions directly improve placement rates. According to Gen Re's 2025 survey, 78% of applications approved through an accelerated workflow were ultimately placed, compared to 63% for fully underwritten cases. When cycle times slow, applicants may lapse, withdraw, or accept a competing offer from a carrier that moved faster, particularly healthy applicants who qualify for multiple carriers' accelerated underwriting programs simultaneously.

‍¹ Gen Re, 2025 U.S. Individual Life Next Gen Underwriting Survey

Melissa Butt, Vice President E&S Brokerage, Property, at Skyward Specialty, has spent more than 20 years underwriting commercial property. She's been using the AI Underwriter on live submissions and we asked her what that’s been like.

First impressions

Melissa's assumption going in was that AI in underwriting meant workflow or operational automation, scrubbing data, prefilling fields. That assumption wasn't wrong, exactly. The tool does cut out a lot of manual legwork:

"It eliminated the screen jumping. You don't have to go from one system to another. Instead, it’s putting it all together so that you're reviewing the pertinent information, with the flexibility to align that information to your viewpoint."

For Melissa, what really changed her day-to-day life was what the AI Underwriter does after it's pulled relevant facts together, when it gets to the real underwriting work. It arrives with underwriting knowledge already built in, then layers in what it learns about a specific carrier's appetite and each underwriter's way of working. That combination is what builds a narrative shaped to underwriters’ own judgment:

"It applies what it knows and what it’s learned from you into a narrative aligned with your individual underwriting point of view."

That narrative comes paired with recommended next steps, and Melissa said she's acting on the next step recommendations more often than not. The AI Underwriter isn’t stopping at an appetite-aligned score or summary, it’s working through real underwriting considerations and next steps: declining a bad fit, flagging what's missing from a broker, initiating a referral.

"I frequently agree with the next steps. Maybe you have missing information and need to reach out to your broker, or an exposure is in a gray area and you should consult a peer or your guidelines. It can trigger a referral and help you narrate it, saving time, energy, clicks. All underwriters want to save clicks."

"Wow, it really knows underwriting” moments

When asked what moments stood out that made her excited to continue using Sixfold, here’s what Melissa had to say.

On a rare occupancy type her market doesn't see often:

"I got a very rare occupancy in one of my submissions, food irradiation. We don't see those a lot, even in our market. What really surprised me was not only did it understand what the food irradiation process was, it allowed me to understand the process, too. It said here's some additional information you may want or need, and here's some questions to go back and get answered from your broker."

On a routine loss-history check, where the AI Underwriter pulled in some surprising research:

"The system scrubbed the loss history we received, but it also went out and looked at the web for additional information. What it discovered was that the property was one of the few buildings on the block that did not recently have a fire, which was significant when writing property insurance."

A favorite feature

Melissa was most excited about the AI Underwriter’s chat interactivity:

"The chat interaction is huge. If I had to point to one thing that makes this tool the most functional, it’s the chat."

The reason: it lets her resolve a question the moment it comes up, without leaving the platform. The chat is connected to the same underlying analysis built for that submission, so digging deeper or providing additional context feeds right back into the narrative and next-step recommendations, as well as the AI Underwriter’s overall memory. 

On one submission, the initial read on a recycling-related exposure was that the business was a metal processor. Melissa concluded it was actually a transfer station, and used the chat to guide the AI Underwriter in a new direction:

"I let the chat know it isn't a true recycling exposure, it's actually a transfer station, and asked how that changes the loss history, the rating, the narrative. It's similar to talking to a peer: hey, I'm not sure, what do you think? And the system responds."

Advice for a skeptical peer

We asked Melissa what she'd say to another underwriter who isn't sure what to make of something like this:

"Don't be afraid. Yes, it's technology. Yes, it's new. But it's not here for your job. It's here to make your job more efficient and to make your experience stronger. Treat it like you've treated every other tool in your arsenal as an underwriter: challenge it, try to break it, because then it's just going to become stronger. It's a tool. It's a peer. It will interact with you so that you are a stronger underwriter."

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Learn more about Sixfold’s latest P&C offering, the AI Underwriter.

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