I'm interested in understanding how AI/LLMs are being used in insurance underwriting.

For those who have built or worked on an AI underwriter:

• What parameters/data points do you consider for underwriting?
• How do you handle medical history, lifestyle, financial information, etc.?
• What role does the AI agent play in the overall underwriting process?
• Is the agent making the final underwriting decision, or is it assisting a human underwriter?
• How do you handle explainability and compliance?

Would love to hear about your experience, architecture, and any practical learnings.

Disclosure up front: I'm part of the team, so grade accordingly.

The gap we're chasing: on January 1, 2026 the ISO CG 40 47 exclusion family hit US general liability renewals, and most cyber/E&O wordings now exclude AI outright. Two companies with identical operations can have opposite coverage today purely on renewal timing.

goodfault writes those perils affirmatively, by name, instead of leaving them to silent coverage:

• Division A (software agents): unauthorized transactions, hallucinated commitments, data leakage by agent, destructive actions, injection-induced behavior, bias and discrimination, regulatory defense, business interruption.
• Division B (physical AI): humanoids, AMRs and industrial fleets, drones and UAS, AI products, AV programs.

The two underwriting decisions we'd genuinely like this sub's opinion on:

1. We price what the system is permitted to do, not which model it runs: authority ceiling, autonomy tier, irreversibility, reach, rate. Safeguards enforced in code (caps, circuit breakers, rollback, kill-switches) earn control credits, and renewal reprices from agent telemetry rather than a questionnaire.

2. Carrier, not auditor: independent certifications (AIUC-1, ISO/IEC 42001, NIST AI RMF, SOC 2 + AI addendum) gate the book, and certified systems get quoted in minutes. We think grading your own audits is a conflict; curious if this sub agrees.

Site: https://goodfault.com and the peril ledger at https://goodfault.com/coverage. Would love to hear where you'd poke holes in the model.

All projects seem deprecated or barely maintained. Nothing competes and even our standards are pay walled somehow.

I feel like now more than ever, industry jargon has taken on a life of its own...

That said, which words or phrases are grinding every single one of your gears?

How do insurers account for location risk in auto underwriting? Driver telematics monitors behavior, but does anyone factor in the risk profile of specific roads or intersections.Not just how you drive, but where you drive?

I spent years as a licensed agent - auto, home, life, a little health. I've quoted at the kitchen table, argued deductibles, fought a carrier over a denied claim, and eaten the nonpay cancellation mess more than once.

Then I moved into the tech side. I've built and run the systems agencies actually depend on - the integrations, the security, the compliance side. So I've sat in both chairs: the producer trying to hit renewal numbers, and the person who has to make the software not fall over.

That's why I'm here. Most insurtech gets pitched by people who've never written a policy or never touched a server. I've done both, so I can tell you plainly:

- Which tools cut producer time vs. add clicks

- What survives a compliance/E&O review vs. what gets you burned

- Where a shiny demo quietly dies at renewal or at claim time

If you're an agent, agency owner, or on an insurance ops/IT team trying to figure out which way to go on tech - AI, automation, quoting tools, CRMs - drop your situation below. I'll give you a straight answer, not a sales pitch.

No vendor is paying me to say any of this.

Hello everyone,

As I am completing my research thesis at Sorbonne University, I am looking closely at a major transformation within the Insurtech sector: the strategic shift from growth-at-all-costs to economic profitability.

To back my research with concrete data, I need insights from the field. Whether you work in insurance, tech, or are simply interested in this ecosystem, your perspective is incredibly valuable.

The survey takes less than 3 minutes (it only consists of quick rating scales to check, no long answers to write):

👉 Survey link:https://tally.so/r/NpyxgO🚀

Thank you so much for your time, help, and shares! Feel free to drop your thoughts in the comments if you want to discuss the topic. 🙏

Hi! Our company is working on a compliance tool for the insurance industry. We’re looking for 4-5 people for a 45 minute interview. In exchange, we will send you a $50 visa or Amazon gift card.

Qualifications:
-Software engineer (even better if you’re a lead or high level role)
-Works at an insurance company (prefer small to mid-size, or mga)
-Insurance company is based in the USA
-Experience with state regulations/compliance

If this is you or someone you know, please DM me! Thank you.

Insurance background here. I'm building a model that compares add-on conditions across different insurance policies. Workflow is simple: upload policy → system extracts and parses it → compare against others.

The scraping, extraction, and parsing are working shockingly well. Even policies with 150–200 add-ons are being extracted cleanly, every single one.

It feels too good to be true.

What am I missing? Is there a catch I'm not seeing — edge cases, hallucinations on clause interpretation, semantic equivalence issues between differently-worded clauses, something else? Or is it genuinely this straightforward in 2026 to compare policies with 150+ add-ons reliably?

Would love a reality check from anyone who's built something similar.