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How three years at McKinsey shaped my second startup

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Re: How three years at McKinsey shaped my second startup

#131

Post by sethhochberg »

sokoloff wrote: post #129
sethhochberg wrote: post #96

Earlier quoted context omitted.

I work in the industry at a startup insurtech, we are a life insurance carrier (wysh.com - our flagship product is a b2b micro life insurance benefit, but we built that on top of a term life carrier and also sell d2c term life) Allianz has ~150k employees but certainly they don't all work on the term life business in the USA, they do all kinds of other insurance stuff all over the world and have hundreds of different…

> You'd probably be shocked how common it is for former-spouses to try and take out insurance policies without the other knowing during divorces. If they were receiving spousal support (“alimony”) or child support, this seems unsurprising and sensible.

The important detail there is doing it without the knowledge of the (former) spouse.

You need both an insurable interest and consent of the insured in order to buy an insurance policy on someone else’s life.

Couples separating and holding policies on each other is pretty common and carriers have some specific rules to follow to make sure there’s appropriate mutual consent for policy changes etc

Re: How three years at McKinsey shaped my second startup

#132

Post by crazygringo »

ben_w wrote: post #64
crazygringo wrote: post #48

Earlier quoted context omitted.

> that the only way to provide dispute resolution and customer service to 1B people with only 100 employees is by depriving them of any chance to interact with a human, and forcing all interaction with the company to go through AI. That, to me, is deeply disturbing, and very very difficult to justify. I don't know. Given the human beings I've interacted with in customer support, and the number of times I've had to es…

While I get the vibes, and have had experience of human customer support being very weird on a few occasions, replacing mediocre humans with mediocre AI isn't a win for customers getting actual solutions. And right now, the LLMs aren't really that smart, they're making up for low intelligence by being superhumanly fast and able to hold a lot of context at once. While this is better than every response being from a ra…

> replacing mediocre humans with mediocre AI isn't a win for customers getting actual solutions.

No it's not, but that's not what I described. I described replacing mediocre humans with better AI for at least the first level of customer service.

Re: How three years at McKinsey shaped my second startup

#133

Post by throw10920 »

siliconc0w wrote: post #128
throw10920 wrote: post #6

Article is interesting on the whole (I have no experience with "professional" work, and would love for suggestions as to how to be more familiar), but I latched onto this nugget: > Our vision at Meanwhile is to build the world's largest life insurer as measured by customer count, annual premiums sold, and total assets under management. We aim to serve a billion people, using digital money to reach policyholders and a…

AI adjudication of healthcare is fine but there needs to be extremely steep consequences for false negatives and a truly independent board of medical experts to appeal to. If a large panel agrees the denial was wrong, a penalty of 10-100x the cost of procedure would be assessed depending on the consequence of the denial.

Yes, I agree. My point was contingent on the current state of affairs - until we can change that, then AI remains a terrible idea.

Re: How three years at McKinsey shaped my second startup

#134

Post by reassess_blind »

siliconc0w wrote: post #128
throw10920 wrote: post #6

Article is interesting on the whole (I have no experience with "professional" work, and would love for suggestions as to how to be more familiar), but I latched onto this nugget: > Our vision at Meanwhile is to build the world's largest life insurer as measured by customer count, annual premiums sold, and total assets under management. We aim to serve a billion people, using digital money to reach policyholders and a…

AI adjudication of healthcare is fine but there needs to be extremely steep consequences for false negatives and a truly independent board of medical experts to appeal to. If a large panel agrees the denial was wrong, a penalty of 10-100x the cost of procedure would be assessed depending on the consequence of the denial.

No one is going to accept a claim rejection from AI. Everyone will want to dispute, which will have to go to a human to review. At the end of the day I don’t see how 100 people is realistic.

Re: How three years at McKinsey shaped my second startup

#135

Post by mring33621 »

reassess_blind wrote: post #134
siliconc0w wrote: post #128

Earlier quoted context omitted.

AI adjudication of healthcare is fine but there needs to be extremely steep consequences for false negatives and a truly independent board of medical experts to appeal to. If a large panel agrees the denial was wrong, a penalty of 10-100x the cost of procedure would be assessed depending on the consequence of the denial.

No one is going to accept a claim rejection from AI. Everyone will want to dispute, which will have to go to a human to review. At the end of the day I don’t see how 100 people is realistic.

Fine! You win!

We'll send the appeals through Mechanical Turk.

Happy now?

Re: How three years at McKinsey shaped my second startup

#136

Post by tbrownaw »

burningChrome wrote: post #80
throw10920 wrote: post #6

Article is interesting on the whole (I have no experience with "professional" work, and would love for suggestions as to how to be more familiar), but I latched onto this nugget: > Our vision at Meanwhile is to build the world's largest life insurer as measured by customer count, annual premiums sold, and total assets under management. We aim to serve a billion people, using digital money to reach policyholders and a…

>> that the only way to provide dispute resolution and customer service to 1B people with only 100 employees is by depriving them of any chance to interact with a human. Real world evidence supporting your argument: United Health Group is currently embroiled in a class action lawsuit pertaining to using AI to auto-deny health care claims and procedures: The plaintiffs are members who were denied benefit coverage. The…

Seems to me that the use of AI is irrelevant[1], and the real problem is the absurd error rate.

[1] In the sense of "it doesn't matter if it caused the problem", rather than "it probably didn't have any effect". Because after all, "to err is human, but to really foul things up takes a computer".

Re: How three years at McKinsey shaped my second startup

#137

Post by roenxi »

tkluck wrote: post #124
thenewwazoo wrote: post #109

Earlier quoted context omitted.

> 90% error rate, meaning nine of 10 appealed denials were ultimately reversed. This is a fantastic illustration of selection bias. It stands to reason that truly-unjustified (some hidden variable) denials would be appealed at a higher rate and therefore the true value is something less than 90%. That's not to say UHG are without blame, I just thought this was really interesting.

Your scientific take is useful in the case where selection bias is unavoidable and needs to be corrected for. This case is not like that; if the insurance agency wants to dispute the 90% false denial rate, it would be trivial for them to take a random sample of _all_ cases, go through the appeal process for those, and publish the resulting number without selection bias. As long as that doesn't happen, the most logica…

The insurance company may well have already done that; this is being put by someone who is suing them and looking for reasons that the AI bot is bad. The article is silent on what the company response to the accusation was and, realistically, we'd expect the appealed denials to have a very high rate of error whether determined by bots or humans. Few people indeed are going to waste time arguing a hopeless case against an insurance company - this is classic selection bias.

Re: How three years at McKinsey shaped my second startup

#138

Post by tiffanyh »

> Meanwhile: to break into a highly-regulated, commoditized market like insurance, you need both a truly differentiated product that incumbents can't easily replicate and an associated distribution strategy that leverages their blind spots.

Having worked in highly regulated industries, I’ve learned that the best way to disrupt incumbents is by creating a product that assumes more business risk than is typically accepted. Large, regulated companies are extremely risk-averse—so if you can take on that risk in a smart, innovative way, you’ll win.

Re: How three years at McKinsey shaped my second startup

#139

Post by azemetre »

Raidion wrote: post #38
guywithahat wrote: post #26

Earlier quoted context omitted.

The whole business is nonsensical. The point of a consultant is they have a lot of experience in a specific domain, a recent Harvard grad is useless. From what I've heard, tons of their consultants are young people with minimal real industry experience

You pay for one or two people with real experience and 4 reasonably new hires whose job it is to answer questions posed by the senior team and to build documentation. You want the senior people focusing on the problems, strategy, and comms and not data aggregation and power point formatting. Half the time it doesn't actually matter who the consultant is, the business is just looking for an arbiter to provide a second…

How does this not vindicate their viewpoint? Do you really need a team of ivy grads to make power points or inexperienced people to give unqualified answers?

Modern consulting seems like one of the better deaths inflicted by GenAI. The entire industry is a means to commit corporate espionage legally.

They can do something more useful with that education.

Re: How three years at McKinsey shaped my second startup

#140

Post by thwarted »

nradov wrote: post #108
thwarted wrote: post #87

Earlier quoted context omitted.

One might argue that the government allocating some resources is more efficient than the market doing so purely because specific outcomes are desired that the invisible hand is not motivated or incentivized to provide. If the goal is to keep people healthy, efficiency is based on how successful that is, not on the monetary cost. Few people seem to understand it this way, though.

In most cases government employees simply aren't prescient enough to allocate resources efficiently. Like in theory maybe central planning could be more efficient if everything worked correctly, but in practice it never works efficiently at scale. Much of the resources simply end up wasted.

If one looks to "government employees", as individuals, then yes, they aren't prescient enough to allocate resources efficiently. But comparing the free market to government employees is not an apples to apples comparison, because individuals don't allocate resources efficiently either in a free market; the "market" as a whole is what optimizes for efficiency.

And I think there is a distinction in different kinds of efficiency that can be optimized for, not just monetary cost. If we desire clean, paved, safe roads, that can be used by all equally for efficient movement of goods, because we recognize that as a prereq for a strong economy, we can not rely on the free market to deliver that, much less optimize for it. It can be more efficient, in terms of actually delivering the desired goal vs not delivering it at all (or delivering a grossly bastardized version of it) to pool our resources and explicitly work towards making something available rather than hoping that the free market will deliver it.

The free market did not deliver on reducing congestion in New York (in fact, one might say that over the decades, the free market is what made it worse), but the congestion pricing program has, and has resulted in a bunch of valuable/desirable knock-on effects.

I do not think that a centrally planned economy is workable; but collectively being deliberate about building the things we need/want, and taking a longer view, can result in significant efficiencies.

The free market ends up simply wasting resources in its drive to discover where efficiencies lie and how to take advantage of them.

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