Full Episodes | Insurtech Leadership Podcast

Introduction

Why does an industry that talks about digital transformation still pay so many claims by paper check? The answer is not stubbornness; it is complexity, and it is worth billions. Josh Hollander sits down with Kevin Ostrander, Chief Revenue Officer at One Inc, to unpack what it actually takes to move carriers off checks, why multi-party claims payouts are the hard part nobody solved first, and what happens when enough carriers and payees are connected that the network, not the processing, is what carriers are buying.

Guest Bio

Kevin Ostrander is Chief Revenue Officer at One Inc, the insurance-specific payments network processing over $245 billion in annual payment volume for more than 300 carriers. He has spent nearly sixteen years selling enterprise technology into insurance, including six years at Thunderhead before its acquisition by Accel-KKR, and he joined One Inc ten years ago when the company had single-digit customers. He owns everything from new business acquisition through customer success.

Key Topics

-Why checks persist - More than half of claims payouts go to third-party vendors, lienholders, and mortgagees, and fifty states each add their own compliance rules, so single-party digital solutions never covered enough of the workflow.

-The malleable ROI case - Checks cost $6 to $25 each fully loaded, digital claim payouts land in minutes instead of days, and the same platform argument works whether the carrier's priority that year is cost, retention, or customer experience.

-Adjuster adoption decides outcomes - If issuing a digital payment adds manual steps, adjusters revert to checks, so One Inc pairs its technology with change management, FAQs, and training on the carrier side.

-One platform for pay-in and pay-out - Over 40% of One Inc's carriers now run both PremiumPay and ClaimsPay, giving policyholders one wallet across premium payments, refunds, and claims.

-The network is the moat - One Inc's escrow network covers 80%+ of escrowed homeowner premium and connects it to 300+ carriers, moving premium 11 to 12 days faster than the 12-to-15-day lockbox process.

-Disciplined expansion - Life insurance (AAA Life, Transamerica) and Canada (with Guidewire, which holds 80%+ of Canadian claims administration) came only after the P&C base was solid.

-Career advice from a first-time CRO - Kevin's path from single-digit customers to 300+ came down to experience you cannot skip, mentors, and recognizing product-market fit when you have it.

Notable Quotes

"Fifty plus percent of the payouts in the insurance industry actually go out to third-party participating vendors."

"The cost of a check fully loaded, including service, print, mail, et cetera, is anywhere from six dollars to twenty-five dollars a check."

"We're actually increasing the speed of premiums by sometimes eleven to twelve days."

"The one thing you can't skip in your career is experience. And you've got to find mentors that will help you through that process."

Resources

Guest:

One Inc: https://www.oneinc.com/

Kevin Ostrander on LinkedIn: https://www.linkedin.com/in/kevin-ostrander-6894ba2/

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show

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Introduction

Building the model is no longer the hard part of insurance AI. The hard part now is getting underwriters and carriers to actually adopt it. Josh Hollander sits down with Roger Ferrandis, Head of Partnerships at Sixfold, whose AI now runs inside carriers like Zurich, Guardian, and Skyward Specialty, to talk about how trust actually gets built: proof-of-concept by proof-of-concept, underwriter by underwriter, and market by market through partners who already have the relationships.

Guest Bio

Roger Ferrandis is Head of Partnerships at Sixfold, the AI underwriting platform. Originally from Spain, he spent seven years in the UK, where he co-founded WeAnalyze, an underwriting data startup he helped grow to 25 employees, $3 million raised, and clients on three continents before it pivoted to banking. He chose insurance over banking, moved to New York, and now runs Sixfold's global partner strategy across systems integrators, reinsurers, data providers, and cloud marketplaces.

Key Topics

-Adoption is the product problem now - Leadership teams know they need AI, hand it to someone to evaluate, and that person's reference point is ChatGPT making mistakes, so skepticism is the default starting position.

-How trust gets built in a POC - Sixfold runs known submissions through its AI, compares decisions with the underwriters, feeds their reasoning back into the model, and iterates until 90-95% of outcomes match, which is when confidence flips.

-Amplify, not replace - Sixfold is not chasing full automation; the goal is an underwriter handling four submissions in the time one used to take, with clients choosing their own comfort level on straight-through processing.

-The ROI stack - Clients measure 50% time saved per submission, quote-to-bind up 15%, and gross written premium per underwriter up 30%.

-Partnerships as market entry - Munich Re's Realytix platform, Microsoft's Azure marketplace, Adnovum in Switzerland, and Sollers Consulting in Poland each open doors that direct sales cannot, especially across Europe's twenty-plus selling cultures.

-Build versus buy, honestly argued - Sixfold's model has trained on nothing but underwriting for three years, and Roger's warning to carriers building in-house is that the technology shifts faster than an internal build can keep up with.

-Involve the underwriters - His advice to carrier leadership: do not delegate AI evaluation and hope for the best, because if underwriters do not buy in, nothing happens.

Notable Quotes

"We can sell to C-levels. But if underwriters are not happy with our solution, nothing's going to happen."

"With that iteration, we get to a point where at least ninety or ninety-five percent of the outcomes of the brain are the same outcomes the underwriter will have. When they see that level of accuracy, that's when we gain their confidence."

"If an underwriter used to deal with one submission every hour, we want them to be able to deal with four."

"By the time you build something yourself, the whole technology has changed so much that you need to rebuild it."

Resources

Guest:

Sixfold: https://www.sixfold.ai/

Roger Ferrandis on LinkedIn: https://www.linkedin.com/in/rogerferrandis/

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show

Subscribe & Review

If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Podbean, Apple Podcasts, and Spotify.

Introduction

What happens when the lawyer who spent his nights cleaning up coverage disputes decides to build the software himself? Most insurtech is built by engineers learning insurance, and Dan Schuleman came at it the other way. He practiced insurance coverage law, watched a late claim letter turn into a bad-faith fight, and built Qumis so the people reading policies actually understand what the words mean.

Guest Bio

Dan Schuleman is co-founder and CEO of Qumis, the Chicago company building attorney-trained AI that reads and interprets insurance policies for brokers, underwriters, and claims teams. He spent his early career as an insurance coverage attorney at Am Law 200 firms, advising carriers and policyholders on high-stakes commercial claims, then became an early legal hire and Associate General Counsel at Kin Insurance, now a unicorn. Qumis raised an oversubscribed $4.3 million seed in February 2026, after a $2.2 million pre-seed, and its technology is used by five of the fifteen largest U.S. brokers, including NFP and Brown & Brown.

Key Topics

-Claims adjusters are practicing law. An adjuster without a law degree still reads a legal contract every day and forms an opinion on how a court would interpret it, which is the overlap Qumis is built around.

-The coverage letter that went out late. Dan traces Qumis back to a hotel roof claim that turned into a bad-faith dispute because the letter did not go out in time.

-The bench of digital experts. Ask Qumis a question and a lead agent assembles specialist agents that each examine the policy and then synthesize one cited answer, the way a well-resourced firm puts a team on a file.

-A 97% lawyer-agreement rate, with citations. Every output traces back to the source text and the reasoning behind it, so an adjuster can agree or disagree instead of trusting a black box.

-Where AI stops and a lawyer starts. Dan calls coverage interpretation one of the hardest things to automate, and he does not see humans leaving the process any time soon.

-Insurance's spreadsheet moment. He compares the shift to accountants and Excel, where the tabulating goes away and the judgment and creative work expand.

-Commodifying routine coverage counsel. Routine coverage questions that get outsourced to outside counsel are the part Dan expects AI to absorb first, changing when and how firms engage lawyers.

Notable Quotes

"AI isn't going to replace humans, but humans using AI will."

"The product is a promise, and then the promise is expressed in a whole bunch of legalese."

"I saw it play out in the claims context, where a comma could mean a million bucks."

"I have 500 pages of PDF on my desk, and I need to spend the next eight hours figuring out what the issues are and getting the letter out. We can turn that down into half an hour, with likely a more accurate output."

Resources

Guest:

Qumis: https://www.qumis.ai

Dan Schuleman on LinkedIn: https://www.linkedin.com/in/danielschuleman/

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show

Subscribe & Review

If this episode was useful, subscribe and leave a review. The Insurtech Leadership Podcast is on YouTube, Podbean, Apple Podcasts, and Spotify.

Introduction

Captive premiums have crossed roughly $79 billion globally, so why are so many captive programs still managed in Excel and email? In this episode, Joshua Hollander talks with Illia Pinchuk, CEO and founder of DICEUS, about where manual workflows leak value in a captive program and what it takes to move a captive manager off spreadsheets. Listeners will hear how feature-based pricing changes the cost math for captives, why data onboarding is the real adoption barrier, and where AI actually helps today.

 

Guest Bio

Illia Pinchuk founded DICEUS in 2011 and has led the company for fourteen years, evolving from professional services for insurers, brokers, MGAs, and TPAs into ready-made products. DICEUS now offers 22 products across business lines, with captive insurance among its strongest segments, anchored by a captive management platform and a captive owner portal. Pinchuk trained as an engineer in mechatronics and robotics and has completed executive coursework through Harvard Business School Online.

 

Key Topics

-A young market with about 7,000 captives - Roughly half of the world's registered captives sit in the US, with Bermuda, Cayman, and a developing UK regime behind it, and business processes remain largely unstandardized.

-Why captive managers can't scale on spreadsheets - The consultant headcount a captive manager needs grows roughly in proportion to the captives it onboards, which turns a service business into a people business.

-Where value leaks in a manual captive - A/B fund premium allocation, loss runs arriving from 10 to 20 different TPAs in incompatible spreadsheets, investment reconciliation, and end-of-period compliance workbooks.

-Feature-based pricing, not module-based - Clients pay only for the specific features they use, which matters in a segment where the entire case for a captive is cost.

-The measurable payoff - Clients report the platform saves the work of one to one and a half business analysts or captive consultants, and policy issuance that once consumed a dedicated person compresses to about a week and a half.

-Key-person risk in Excel - When the one employee who knows the spreadsheets leaves, the knowledge leaves with them; built-in onboarding and AI guidance get a replacement productive in one to two weeks.

-AI needs clean data first - DICEUS runs the full extract, transform, load process itself, cleaning years of unstructured spreadsheet data before the platform's AI features can pay off.

 

Notable Quotes

"They cannot grow without adding new people. So it becomes more people business than just a service business."

 

"At the end of the month you're getting twenty completely different spreadsheets, and you somehow should make a magic work in order to calculate the loss ratio."

 

"Our system can save, for a traditional captive manager organization, at least one or one and a half consultants. But in reality, I think it's even more."

 

"They should pass through the digital onboarding step, just filling in all the details, and it should be automatically enrolled. Like, for example, if you want to open a bank account. That will be a big game changer."

 

Resources

Guest:

DICEUS: https://diceus.com/

Illia Pinchuk on LinkedIn: https://www.linkedin.com/in/illiapinchuk/

 

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show

 

Subscribe & Review

If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Podbean, Apple Podcasts, and Spotify.

Introduction

What happens to a $114 billion market when six people head toward retirement for every one person coming in behind them? That is the question hanging over the MGA channel, and it sits at the center of Vertafore's 2026 MGA Workforce and Technology Report. Josh Hollander sits down with Emily McGinn, who runs Vertafore's MGA & Wholesale business unit, to dig into what a survey of nearly 200 MGA leaders says about succession, operational discipline, and where AI is already paying for itself.

Guest Bio

Emily McGinn is SVP & General Manager of the MGA & Wholesale business unit at Vertafore, one of the largest insurance technology providers in North America, where she runs product development, professional services, and customer service for the segment. She came to insurance nine months ago from telecom and technology operations leadership at Zayo, Lumen, and Windstream, which gives her an outsider's read on an industry that keeps its people for decades.

Key Topics

-The demographic cliff - 67% of the MGA workforce is 44 or older, only 4% is under 28, and the ratio of workers 55-plus to workers under 25 runs about six to one.

-What walks out the door first - Carrier and agency relationships built over three or four decades, plus underwriting judgment that has seen full market cycles, are the things technology cannot replace.

-Growth is moderating, and it changes the playbook - MGA premium growth has cooled from double digits to single digits, so premium volume alone no longer covers for thin operational discipline.

-Operational excellence is now a carrier mandate - Carriers are demanding clean data, real governance, and bordereau reporting that ties out, and they are choosing MGA partners accordingly.

-Where AI pays for itself today - Vertafore's email ingestion agent reads unstructured submissions and fills the application automatically, turning 20 minutes of data entry into a three-minute review.

-Tech-first MGAs are table stakes, not a threat - The startups are pushing everyone to move faster, but the winners still need the industry foundation first and the technology on top.

2026 as the adoption inflection - 21% of surveyed MGAs use AI today and 50% plan to adopt soon, and Emily believes those numbers were already stale within months of the survey.

Notable Quotes

"The ratio is about six people 55-plus to one person under 25."

"It used to take you twenty minutes to read these PDFs and data-entry this all in. Now it takes seconds. You review it in three minutes to make sure it's right and move on."

"If you just start with the technology, you're probably not going to win."

"The MGAs that didn't make it, it was often a data issue. They lost the carrier's trust."

Resources

Guest:

Vertafore: https://www.vertafore.com/

Emily McGinn on LinkedIn: https://www.linkedin.com/in/emily-dempsey-mcginn/

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show

Subscribe & Review

If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Podbean, Apple Podcasts, and Spotify.

Introduction

What makes an underwriter act on an AI's answer? Not the accuracy of the model, according to Stan Smith, but whether the person can see how the model got there. Josh Hollander sits down with the founder and CEO of Gradient AI, whose platform is trained on a contributory data lake of tens of millions of policies and claims, to talk about explainability as the last mile of AI adoption, what regulators actually want to see, and the A/B test that put a hard dollar figure on AI-managed claims.

Guest Bio

Stan Smith is the founder and CEO of Gradient AI, which builds AI that insurers use to underwrite risk and manage claims across both P&C and health. He started the business inside Milliman, bought it out in 2018, and has since raised roughly $90 million in growth capital and grown from about a dozen clients to several hundred. Before Gradient, he built a machine-learning startup that predicted supplier performance from pooled supply chain data, the same contributory model that now powers Gradient's data flywheel.

Key Topics

-Explainability is the last mile - A correct number the underwriter cannot interrogate gets ignored, and a number with visible reasoning gets used, disagreed with productively, and trusted over time.

-What regulators actually want - They are not auditing the math; they regulate inputs and outputs, with the sharpest focus on personal lines, like Massachusetts barring personal credit in personal auto underwriting.

-GLMs versus AI - Linear models stay popular because they are explainable, but they miss subtleties in the signal, and Stan argues that trade-off costs accuracy the industry does not have to give up.

-The $7 million A/B test - A large self-insured employer held part of a roughly 20,000-claim book out of Gradient's claims management and measured $7 million in savings on the AI-managed side.

-The contributory flywheel - Clients share data because Milliman-era trust made it safe, and the pooled data makes every client's models better, which is what in-house builds cannot replicate.

-The MVP trap - Carriers that build internally usually stall at a minimally viable product, over budget and behind schedule, while vendors iterating across hundreds of clients keep compounding.

-What got us here won't get us there - Stan's scaling mantra: priorities, execution discipline, and accountability have to change every year, without losing startup speed.

Notable Quotes

"If the person is not confident as to how the model came to that conclusion, they can just pass, even though the model might have given them some important directional information."

"They measured a seven million dollar improvement in their loss costs on the claims we were managing versus the claims in their A test."

"What they build in-house tends to be a minimally viable product. They've told me this. I haven't said it to them."

"My constant mantra to the team is: what got us here won't get us there."

Stan Smith

Resources

Guest:

Gradient AI: https://www.gradientai.com/

Stan Smith on LinkedIn: https://www.linkedin.com/in/stan-smith-5029246/

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show

Subscribe & Review

If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Podbean, Apple Podcasts, and Spotify.

Introduction

Most insurance executives have heard the AI pitch a hundred times by now, and most of those pitches collapse the moment they hit a real call center, a real compliance team, or a real broker's quoting workflow. The interesting question is no longer whether AI works in insurance. The question is what deployment method survives a regulated, audited, multi-channel customer operation, and which vendors actually know how a policy gets quoted, bound, and serviced.

In this episode, Joshua R. Hollander sits down with Pablo Molina, CEO and co-founder of Infinite Watch, to unpack what AI-native infrastructure looks like inside a carrier or broker. Molina argues for a method that starts with observability rather than automation, and for a vendor selection criterion that has shifted from technology to domain expertise and traceability.

Listeners will come away with a clearer view of how to sequence an AI deployment in a regulated business, why a hundred percent visibility into customer interactions is now table stakes, and what Molina sees coming next for the workforce.

Guest Bio

Pablo Molina is CEO and co-founder of Infinite Watch, an AI-native platform built for insurance and other regulated verticals. Infinite Watch operates three families of agents: conversational voice, data and document handling, and real-time business intelligence. The company came out of stealth in late 2025 and is running production deployments in personal lines, commercial lines, and payment collections across the US and Europe.

Before Infinite Watch, Molina was the founding CTO at CoverWallet, where he scaled engineering past 300 people and helped grow the business past one billion dollars in premiums before Aon acquired it in 2019. He is a software engineer with more than a decade of insurance domain experience.

Key Topics

• Observability before automation - Why Infinite Watch deploys insight agents first, ingesting calls, messages, and web interactions before any task gets automated. Operators need a true picture of customer interactions before deciding which parts to hand to an agent.

• From 1-5% audit samples to 100% visibility - Carrier and broker QA teams sample one to five percent of calls. Molina explains why semantic LLM coverage of every interaction changes the operating model for playbook adherence, cancellation analysis, and missed upsells.

• Where the moat actually lives in AI for insurance - With frontier models commoditizing fast, the differentiator is no longer the technology stack. It is domain expertise and built-in traceability that satisfies regulators on day one.

• Personal lines today, commercial lines next - How Infinite Watch is moving from automated payment collections and end-to-end claims into commercial-lines quote-and-bind, compressing days into minutes.

• AI replacement, not just augmentation - Molina pushes past the safe answer. He argues software engineering was the first profession to feel AI replacement because code is the easiest output to validate, and every profession follows.

• Founding team dynamics on round two - Putting a CoverWallet founding team back together with different titles, hiring small and senior, and building an AI-native engineering culture from day one.

• Three years out for Infinite Watch - Whether agents replace or complement legacy core systems, and which incumbents survive the transition.

Notable Quotes

"We first of all deploy AI insights agents that will ingest every customer interaction. We sit in the front office. Then we have a view of what the operations and customer interaction look like."

"There is no technology moat as such, like it used to be. The differentiators are not in the technology anymore, because people can catch up really fast."

"Audits and traceability is a built-in. There is no other choice. It is not a negotiation point."

"Professions are going to be replaced and reinvented entirely, like a hundred percent."

Resources

Guest:

Infinite Watch: https://infinitewatch.ai

Pablo Molina on LinkedIn: https://www.linkedin.com/in/pablomolinacandel/

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast: https://www.linkedin.com/showcase/insurtech-leadership-show

Subscribe & Review

If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Podbean, Apple Podcasts, and Spotify.

Introduction

What happens to your health coverage the day you leave a job, go independent, or pick up seasonal work? For more than 90 million Americans who earn outside a traditional employer plan, the answer is usually that it disappears. Felix Ortiz, founder and CEO of Smirk Health, joined host Joshua Hollander to explain why the benefits system built in 1929 for full-time employees no longer fits the way people work, and what it takes to build something that does. The conversation covers portable coverage that moves with the worker, modular plans that start at $19, and an AI layer that does more than answer questions.

Guest Bio

Felix Ortiz is the founder and CEO of Smirk Health, an AI health benefits infrastructure company in Austin building portable coverage for 1099, part-time, hourly, and seasonal workers, underwritten and insured by Chubb. He is a repeat founder whose earlier companies spanned education technology, talent intelligence, and a banking-and-insurance platform for Americans of modest means. He is also a U.S. Army veteran and a marathoner who has finished five of the seven World Marathon Majors. Much of Smirk traces back to watching insurers decide his younger brother's care during a childhood heart condition, and to his own coverage gap leaving the military.

Key Topics

The 1929 problem - Group benefits were designed for full-time employees, so the fastest-growing part of the workforce gets priced out or left ineligible.

Coverage that follows the worker - When a member changes employers, the plan, the price, and the benefits stay the same instead of ending with the job.

Modular plans from $19 - Smirk rebuilt the plan chassis so members can add or remove coverage and see what is covered and what it costs before they buy.

From supplemental to fully insured - Smirk took a product category damaged by bad actors, re-engineered it into a fully insured medical plan, and stacked it on an AI layer.

An AI layer, not a chatbot - Because the plan is tied into Smirk's infrastructure, the concierge can take a member from a question all the way to a booked appointment with known costs.

The AI divide - Ortiz argues a gap is opening between people on free AI models and people on paid ones, and that health is the wrong place to let that gap decide outcomes.

Getting a carrier to say yes - How Smirk earned Chubb as an underwriting partner, and what Ortiz tells founders about approaching a large carrier.

Notable Quotes

"Somebody will have a Toyota, somebody else will have a Mercedes-Benz, but ultimately they still have access to a car. And when it comes to health, they don't. That's the big pain point."

"We've gutted the whole thing and re-engineered it to be a fully insured medical plan."

"You actually are starting to get a divide in AI infrastructure. That's a topic no one talks about."

"Three years out, Smirk will be the infrastructure stack for AI within the health and financial intersection."

Resources

Guest:

Smirk Health: https://www.smirkhealth.com

Smirk Health on LinkedIn: https://www.linkedin.com/company/smirk-health

Felix Ortiz on LinkedIn: https://www.linkedin.com/in/fwoiii/

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show

Subscribe & Review

If this episode was useful, subscribe and leave a review. The Insurtech Leadership Podcast is on YouTube, Podbean, Apple Podcasts, and Spotify.

Introduction

Most carriers are still running underwriting on policy admin systems built three decades ago — stitched together through a hundred acquisitions and never designed to work with AI. Federato is betting the only way to fix that is to replace it entirely.

William Steenbergen is the co-founder and CTO of Federato, the first AI-native platform built to cover the full commercial insurance policy lifecycle. He started in reinforcement learning research before spending five months in a cabin in Idaho interviewing underwriters until he understood the problem well enough to build a solution. Federato has since raised $100 million from Goldman Sachs and is now live across commercial lines from SMB to large enterprise.

In this conversation, Josh Hollander and Steenbergen dig into why bolting AI onto legacy systems keeps failing, what the underwriting workflow looks like inside an AI-native platform, and why Federato has started turning away customers who aren't ready to make the full switch.

Guest Bio

William Steenbergen is the Co-Founder and CTO of Federato, an AI-native platform covering the full commercial policy lifecycle — from email submission through rating, quoting, binding, issuance, endorsements, and renewal. He conducted reinforcement learning research at Stanford before co-founding Federato in 2020, spending over a thousand hours interviewing underwriters before writing a line of code. Federato raised $100 million from Goldman Sachs in 2024.

Key Topics

Why legacy systems can't run AI agents — Old core policy admin systems have been stitched together through 100+ acquisitions. The data and tools don't live in a standardized way, making it nearly impossible for AI agents to access the context they need to act — not just summarize.

The three things an AI agent needs — An LLM, context (submission data, product definitions, claims history, forms), and tools it can interact with to take real action. Most incumbents can't provide all three in an AI-native way.

What underwriting looks like now — Ten minutes after an email submission arrives, the underwriter logs in to find it already quoted. They review the AI agent's reasoning, citations, and assumptions, then approve, adjust, or ask follow-up questions in plain text — structurally identical to reviewing a referral.

95% accuracy vs. a room full of humans — Federato ran a study comparing AI agent outputs to human underwriter decisions on the same policies. The agent matched humans 95% of the time and showed less variance than ten humans working the same policy independently.

Turning away the wrong customers — Federato now declines prospects who want to use the platform as a workbench on top of a legacy policy admin system. The only configuration that works is replacing the policy admin system entirely.

AI regulation and accountability — Underwriters still review and approve every AI-generated quote. The AI runs deterministic tools — the rater, the filed forms — it can only make mistakes on the inputs it sends, not the outputs those tools generate.

Notable Quotes

"We're not trying to tack on AI onto an existing process. We're re-envisioning what a good insurance and underwriting process actually looks like."

"When an AI agent interacts with the rater, it doesn't make up the premium. It still runs a deterministic rater. The tool is deterministic."

"If you're not subscribed to doing a full policy lifecycle in Federato and actually replacing your policy admin system, you're probably not the right customer for us."

Resources

Guest:

Federato: https://www.federato.ai

William Steenbergen on LinkedIn: (verify and add URL)

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast: https://www.linkedin.com/showcase/insurtech-leadership-show

Subscribe & Review

If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Apple Podcasts, and Spotify.

Introduction

The rules that governed software pricing for a decade are breaking down. Per-user SaaS models made sense when users were people. Now that AI is automating those seats, the pricing logic collapses — and carriers, brokers, and insurtech vendors are all rethinking the economics at the same time.

Michael Nadel is a partner and head of the global insurance practice at Simon-Kucher & Partners. He joined Josh Hollander for his second appearance on the show to dig into what the AI era is actually doing to monetization — from the pricing mistakes early movers keep making, to why outcome-based models are harder to execute than they are to sell, to a 50,000-session LLM shopping simulation that revealed what could become the next SEO arms race for insurance carriers.

 

Guest Bio

Michael Nadel is a partner and head of the global insurance practice at Simon-Kucher & Partners, a global strategy consultancy focused on growth, pricing, and monetization. Before consulting, he spent time at CNA in strategy and innovation, and earlier led large-scale financial services implementation work at Accenture. He advises carriers, brokers, MGAs, and insurtech vendors on pricing strategy, and co-hosts an annual monetization masterclass at InsureTech Connect.

 

Key Topics

• From offense to defense — A year ago, clients asked how to monetize new AI features. Today the questions are more defensive: how does AI threaten my core product, my pricing model, and my existing revenue base?
• The two biggest AI pricing mistakes — Pricing before understanding what customers actually value, and jumping to outcome-based models before you can define or reliably deliver the outcome.
• Outcome-based pricing is the destination, not the shortcut — Buyers love paying only when value is delivered. The problem is definitional complexity — what counts as the outcome, who controls it, and what happens when results fall short.
• AI spend that looks like RPA in new packaging — Carriers running well-designed AI programs focus on workflow economics, not technology for its own sake. The recommended split: 70% on high-value workflow automation, 20% on data and governance, 10% on exploratory bets.
• GEO: the next SEO — Simon-Kucher simulated 50,000 insurance shopping sessions across 50 consumer personas and three major LLMs. What gets a carrier recommended by an LLM is not the same as what gets them ranked on Google. CMOs need to decide how to treat LLMs as a distribution channel now.
• Services businesses face the same disruption — Pure labor arbitrage is structurally challenged by AI. Firms that survive will combine domain expertise with AI-enabled delivery and evolve from vendors to partners.

 

Notable Quotes

"You need to understand what people value before you prescribe a price. But very often, people build something, bring it to market, and then try to figure out why it isn't selling."

"Automating a bad process simply creates a faster bad process."

"It's not replacing system X with system Y — it's changing the way you work fundamentally. Orange juice to lemonade. Not a better way to make orange juice."

"Last year, roughly one percent of our traffic came from LLMs. This year it was roughly five percent and increasing."

 

Resources

Guest:

• Simon-Kucher & Partners: https://www.simon-kucher.com
• Michael Nadel on LinkedIn: (verify and add URL)

Host & Organization:

• Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/
• Horton International (USA): https://www.horton-usa.com/
• Insurtech Leadership Podcast: https://www.linkedin.com/showcase/insurtech-leadership-show

 

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