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I work in insurance - regulated, human capital heavy, etc. Three examples for you: - our policy agent extracts all coverage limits and policy details into a da

by jdross 9mo ago

I work in insurance - regulated, human capital heavy, etc. Three examples for you: - our policy agent extracts all coverage limits and policy details into a data ontology. This saves 10-20 mins per policy. It is more accurate and consistent than our humans - our email drafting agent will pull all relevant context on an account whenever an email comes in. It will draft a reply or an email to someone else based on context and workflow. Over half of our emails are now sent without meaningfully modifying the draft, up from 20% two months ago. Hundreds of hours saved per week, now spent on more valuable work for clients. - our certificates agent will note when a certificate of insurance is requested over email and automatically handle the necessary checks and follow up options or resolution. Will likely save us around $500k this year. We also now increasingly share prototypes as a way to discuss ideas. Because the cost to vibe code something illustrative is very low, an it’s often much higher fidelity to have the conversation with something visual than a written document

hattmall 9mo ago
Thanks for that. It's a really interesting data point. My takeaway, which I've already felt and I feel like anyone dealing with insurance would anyway, is that the industry is wildly outdated. Which I guess offers a lot of low hanging fruit where AI could be useful. Other than the email drafting, it really seems like all of that should have been handled by just normal software decades ago.
- mjevans 9mo ago
  A big win for 'normal software' here is to have authentication as a multi-party/agent approval process. Have the client of the insurance company request the automated delivery of certified documents to some other company's email.
potamic 9mo ago
> our policy agent extracts all coverage limits and policy details into a data ontology. Are they using some software for this or was this built in-house?
heyitsguay 9mo ago
Thanks for this answer! I appreciate the clarity, I can see the economic impact for your company. Very cool.
ThrowawayTestr 9mo ago
>our policy agent extracts all coverage limits and policy details into a data ontology Aren't you worried about the agent missing or hallucinating policy details?
- tonyedgecombe 9mo ago
  Management has decreed that won't happen so it won't.
  - senko 9mo ago
    What an uncharitable and nasty comment for something they clearly addressed in theirs: > It is more accurate and consistent than our humans. So, errors can clearly happen, but they happen less often than they used to. > It will draft a reply or an email "draft" clearly implies a human will will double-check.
    - tonyedgecombe 9mo ago
      >So, errors can clearly happen, but they happen less often than they used to. If you take the comment at face value. I'm sorry but I've been around this industry long enough to be sceptical of self serving statements like these. >"draft" clearly implies a human will will double-check. I'm even more sceptical of that working in practice.
    - ptx 9mo ago
      > "draft" clearly implies a human will will double-check. The wording does imply this, but since the whole point was to free the human from reading all the details and relevant context about the case, how would this double-checking actually happen in reality?
      - phantasmish 9mo ago
        The “double checking” is a step to make sure there’s someone low-level to blame. Everyone knows the “double-checking” in most of these systems will be cursory at best, for most double-checkers. It’s a miserable job to do much of, and with AI, it’s a lot of what a person would be doing. It’ll be half-assed. People will go batshit crazy otherwise. On the off chance it’s not for that reason, productivity requirements will be increased until you must half-ass it.
stefan_ 9mo ago
That sounds a lot like "LLMs are finally powerful enough technology to overcome our paper/PDF-based business". Solving problems that frankly had no business existing in 2020.
throaway45425 9mo ago
I think we are the stage of the "AI Bubble" that is equivalent to saying it is 1997, 18% of U.S. households have internet access. Obviously, the internet is not working out or 90%+ of households would have internet access if it was going to be as big of deal as some claim. I work at a place that is doing nothing like this and it seems obvious to me we are going to get put out of business in the long run. This is just adding a power law on top of a power law. Winner winner take all. What I currently do will be done by software engineers and agents in 10 years or less. Gemini is already much smarter than I am. I am going to end up at a factory or Walmart if I can get in. The "AI bubble" is a mass delusion of people in denial of this reality. There is no bubble. The market has just priced all this forward as it should. There is a domino effect of automation that hasn't happened yet because your company still has to interface with stupid companies like mine that are betting on the hand loom. Just have to wait for us to bleed out and then most people will never get hired for white collar work again. It amuses me when someone says who is going to want the factory jobs in the US if we reshore production? Me and all the other very average people who get displaced out of white collar work and don't want to be homeless is who. "More valuable" work is just 2026 managerial class speak for "place holder until the agent can take over the task".

by jdross 9mo ago

hattmall 9mo ago

Thanks for that. It's a really interesting data point. My takeaway, which I've already felt and I feel like anyone dealing with insurance would anyway, is that the industry is wildly outdated. Which I guess offers a lot of low hanging fruit where AI could be useful. Other than the email drafting, it really seems like all of that should have been handled by just normal software decades ago.

mjevans 9mo ago
A big win for 'normal software' here is to have authentication as a multi-party/agent approval process. Have the client of the insurance company request the automated delivery of certified documents to some other company's email.

potamic 9mo ago

> our policy agent extracts all coverage limits and policy details into a data ontology. Are they using some software for this or was this built in-house?

heyitsguay 9mo ago

Thanks for this answer! I appreciate the clarity, I can see the economic impact for your company. Very cool.

ThrowawayTestr 9mo ago

>our policy agent extracts all coverage limits and policy details into a data ontology Aren't you worried about the agent missing or hallucinating policy details?

tonyedgecombe 9mo ago
Management has decreed that won't happen so it won't.
- senko 9mo ago
  What an uncharitable and nasty comment for something they clearly addressed in theirs: > It is more accurate and consistent than our humans. So, errors can clearly happen, but they happen less often than they used to. > It will draft a reply or an email "draft" clearly implies a human will will double-check.
  - tonyedgecombe 9mo ago
    >So, errors can clearly happen, but they happen less often than they used to. If you take the comment at face value. I'm sorry but I've been around this industry long enough to be sceptical of self serving statements like these. >"draft" clearly implies a human will will double-check. I'm even more sceptical of that working in practice.
  - ptx 9mo ago
    > "draft" clearly implies a human will will double-check. The wording does imply this, but since the whole point was to free the human from reading all the details and relevant context about the case, how would this double-checking actually happen in reality?
    - phantasmish 9mo ago
      The “double checking” is a step to make sure there’s someone low-level to blame. Everyone knows the “double-checking” in most of these systems will be cursory at best, for most double-checkers. It’s a miserable job to do much of, and with AI, it’s a lot of what a person would be doing. It’ll be half-assed. People will go batshit crazy otherwise. On the off chance it’s not for that reason, productivity requirements will be increased until you must half-ass it.

stefan_ 9mo ago

That sounds a lot like "LLMs are finally powerful enough technology to overcome our paper/PDF-based business". Solving problems that frankly had no business existing in 2020.

throaway45425 9mo ago

I think we are the stage of the "AI Bubble" that is equivalent to saying it is 1997, 18% of U.S. households have internet access. Obviously, the internet is not working out or 90%+ of households would have internet access if it was going to be as big of deal as some claim. I work at a place that is doing nothing like this and it seems obvious to me we are going to get put out of business in the long run. This is just adding a power law on top of a power law. Winner winner take all. What I currently do will be done by software engineers and agents in 10 years or less. Gemini is already much smarter than I am. I am going to end up at a factory or Walmart if I can get in. The "AI bubble" is a mass delusion of people in denial of this reality. There is no bubble. The market has just priced all this forward as it should. There is a domino effect of automation that hasn't happened yet because your company still has to interface with stupid companies like mine that are betting on the hand loom. Just have to wait for us to bleed out and then most people will never get hired for white collar work again. It amuses me when someone says who is going to want the factory jobs in the US if we reshore production? Me and all the other very average people who get displaced out of white collar work and don't want to be homeless is who. "More valuable" work is just 2026 managerial class speak for "place holder until the agent can take over the task".