Depending on your perspective of "ML", the insurance industry already uses "ML" (i.e. very complicated decision trees) to process claims. Very few large insurance companies are non-automated in claims processing.
The places where the money hides, so to speak, include (1) handling complex cases [customers] (2) scaling a human's ability to process non-automatable settlements. (3) scaling internal support interactions with customers (4) introspection to claims data and support data. (5) graceful handling of prior authorizations.
These problems are not as attractive, but they are where insurance companies spend most of their money. It's still a tech problem, but it's not super fancy.
Existing carriers struggle to solve these problems, because they have historically grown by acquisition, and as such do not have the kinds of unified data systems required for the rapid development of applications that perform the required kinds of introspection. It's a space that's ripe for disrupting.
I’d say customer acquisition is the biggest cost and insurance companies are terrible at it because differentiation is almost impossible in a price driven by extreme price war.
It’s not uncommon that 30-50% of your travel insurance premiums are going to a broker or price comparison website. Talking about great value.
It’s not as sexy as ML in claims but one of the big innovations Lemonade has developed is looking like the anti-insurer and creating a huge PR machine around that. True or not, it worked.
I’ve observed this, but I don’t think that any carrier has successfully figured out a way to grow that doesn’t involve brokers (digital or physical). They have a death grip on the market, with a very high percentage of potential members held behind their gate.
Like you, I’m not convinced that the value they add to the chain justifies their expense, but they’re legally and economically entrenched.
My only hypothesis for their eventual dissolution is that unit commissions will get smaller and smaller over time, as more brokers use tech to manage bigger books of business with lower employee headcount requirements, and brokers become more indistinguishable from carriers.
Or maybe some consortium of carriers will get together to build THE comparison shopping site, like healthcare.gov, and offer some ridiculous bonus payment to you the member for shopping there.
Yep, this is what I meant. The "first generation" of these engines were written in COBOL, and are quite nasty by modern standards.
Most people in the tech industry don't apply the ML label to these kinds of software. However, there is a ton of knowledge invested in these systems: certainly more than any one currently-living human has in their head.
The places where the money hides, so to speak, include (1) handling complex cases [customers] (2) scaling a human's ability to process non-automatable settlements. (3) scaling internal support interactions with customers (4) introspection to claims data and support data. (5) graceful handling of prior authorizations.
These problems are not as attractive, but they are where insurance companies spend most of their money. It's still a tech problem, but it's not super fancy.
Existing carriers struggle to solve these problems, because they have historically grown by acquisition, and as such do not have the kinds of unified data systems required for the rapid development of applications that perform the required kinds of introspection. It's a space that's ripe for disrupting.