The Most Interesting Thing Said In Insurnace In Years

Bolt added that his biggest long-term fear for the specialty insurance market was that it too would face challenges from new lower-cost competitors.

“You look at Amazon.com – they’re doing same day delivery. And we don’t get the policy out and it’s just an ephemeral promise to pay and it’s evidenced by an electronic thing and you can’t get it to somebody the same day. And the cost is somewhat expensive to deliver.”

Bolt said that London takes about 30 points of the premium just to deliver the product, including brokerage fees, with around 70 points of benefit baked into the product.

“I’m just really worried that someone will come along – Samsung or someone – and figure it out.”

That’s from the insurance insider (gated). My view is of course that this fear is probably accurate in the bigger sense for insurance but specialty lines are the underwriting heartland. Humans will last longest there.

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Who Creative Destruction Destroys

“We’ve got fifteen people left but without a replacement carrier, they’ll all be gone soon.”

We all knew this company was toast. We had gotten the referral from a prospective client who was the carrier that cancelled them. Talk about the brush off. For all of us.

This guy’s results had been terrible and his strategy was a generation old. The market was minimum limit auto business (called nonstandard), one of the most competitive and efficient markets the world has ever seen. Carriers that win there tend to be extremely technologically sophisticated (cutting edge analytics, highly scaled online distribution), narrowly focused strategically (only auto) and regional (strong relationships in community and with local producers to limit moral hazard). Everyone else gets creamed and these guys were getting creamed.

A couple of my colleagues, bless them, were spending time trying to pull together some information to help. As the lead analyst in the office the pitch on the numbers was inevitably going to be up to me. But we weren’t going to get what we needed, which was simple enough: evidence (data) to support the story that this company could make money (rates are going up!). I knew this guy wasn’t sophisticated enough to have that evidence. He wasn’t even sophisticated enough to ask that question! In the right way anyway.

This means that he not only didn’t make any money but that he didn’t understand how someone could make any money on the business he was doing. Wait until the market turns, he thought, like a sickly old goat wandering into a navy seal firing range.

So we had the call. He sent us all the data he had, clearly working hard at it. But it wasn’t useful and told a terrible story. So we had one more call and said as much. That’s when we got the sob story which to be honest felt awful.

This was a little while ago and I still think about that guy. On a personal level, I hope he’s found something for himself and the people who relied on him. In the bigger picture though, the world is a better place when outdated business models die.

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From Couch Potatoes to Neural Nets

How about we do no less than lay out the levels of modeling sophistication among humans.

Level 1: I don’t understand or care to understand how the world works. People magazine (ESPN for chicks) or ESPN (gossip rag for bros) and I’m done.

Level 2.The world is pretty simple. I don’t bother to even pretend to hide political or cognitive biases. Whatever those are.

Level 3. The world is complicated. If you ignore nuance you lose. Our understanding needs to reflect that complexity so let’s build models that are complicated.

Level 4. The world is complicated but humans can’t handle that. Let’s use heuristics to make good enough decisions but get a lot more done.

Level 5. Hey level 4, you’re more like level 2 than you’d care to admit. Heuristics are fine but use them wisely, know your biases, pass ideological Turing tests. Succumb to your flaws, but consciously.

Balancing the need for complexity with a limited ability to understand it is the biggest challenge in my professional life. And at some frontiers of human knowledge this is the dominant problem (social systems like the business world are different). Some hope that the machines will save us. Computers’ superiority to us in chess is the beginning, they say.

Could be I suppose, but we’re a long way away. Take neural networks, a very sexy topic of late.These are machine learning techniques modeled on the human brain, the killer innovation being that the analysis is done in nodes that all interact with each other.

The thing to take away from that is that wet don’t know what the network is doing to the data in between the input and output. We feed it data and tell it what the output should look like and repeat millions of times. Eventually the program settles in on the steps that minimize error against the output. Again, remember, we don’t know what the program does to the data inside the network. And one of the candidate theories (that the network transforms the data in meaningful ways in its journey through they net) has now been discredited.

This means that neural networks do not “unscramble” the data by mapping features to individual neurons in say the final layer. The information that the network extracts is just as much distributed across all of the neurons as it is localized in a single neuron.

And here’s another result

Since the very start of neural network research it has been assumed that networks had the power to generalize. That is, if you train a network to recognize a cat using a particular set of cat photos the network will, as long as it has been trained properly, have the ability to recognize a cat photo it hasn’t seen before.
Within this assumption has been the even more “obvious” assumption that if the network correctly classifies the photo of a cat as a cat then it will correctly classify a slightly perturbed version of the same photo as a cat. To create the slightly perturbed version you would simply modify each pixel value, and as long as the amount was small, then the cat photo would look exactly the same to a human – and presumably to a neural network.
However, this isn’t true.

So if they take a correctly recognized picture of a cat and change a few pixels the computer no longer recognizes it. Ouch. See the link for pairs of images that are indistinguishable to they human eye but baffling to the net.

One last quote because it is a bit hilariously overcomplicated:

One possible explanation is that this is another manifestation of the curse of dimensionality. As the dimension of a space increases it is well known that the volume of a hypersphere becomes increasingly concentrated at its surface. (The volume that is not near the surface drops exponentially with increasing dimension.) Given that the decision boundaries of a deep neural network are in a very high dimensional space it seems reasonable that most correctly classified examples are going to be close to the decision boundary – hence the ability to find a misclassified example close to the correct one, you simply have to work out the direction to the closest boundary.

I think what this means is that they’re fragile as predictive devices. The net is capable of generating incredible complexity in its treatment of the data with each node interacting with so many others. The term of art for this is overfitting: a high radio of predictive variables to training events. This means if you give it a new test it hasn’t seen before (ie if you actually use the damn thing) it will fail hopelessly.

I am also reminded of Google’s driverless car. When I was leaning machine learning the neural networks section featured this project prominently as an example of NN’s practical use. I’ve since learned that they abandoned this strategy ages ago and now literally program the routes and rules into the software. If it runs into a situation it doesn’t recognize or a route that hasn’t been programmed it stops and waits for a human to take over. Driverless, sure, I guess. But not smart.

I think it was Robin Hanson who wrote something a while ago that stuck in my head but I can’t find on his blog to cite. From memory he said that the more general the situation a system must adapt to the more similar systems look that adapt to it independently. Think about how eyes or wings evolved independently many times. Perhaps our brain, as an incredibly general device, really is as good as a system could possibly be at managing complexity? If we want better performance from another kind of mind we must necessarily then sacrifice something enormous from its capability.

Another way of saying this is that of course neural networks are crap. If they were any good we would be able to understand them!

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A16Z Podcast

It’s my latest favorite. See here.

I work in the insurance business but I find tech news much more interesting and useful to me professionally than insurance news. The reason for this is that the best business minds on the planet are in ‘Silicon Valley’ and they are pushing out the frontier on business innovation as well as technology innovation.

So I don’t learn about insurance from tech people (insurance isn’t changing much) but I do get to think about where disruption might hit insurance companies. A16Z’s latest podcast touches on what the arc of technological change looks like. Uber is a transport company, for example, AirBnB is a travel company.

The key question is what is the technological core of your industry? That is where tech startups will be looking for traction and radical innovation there might completely change your business.

Insurance is composed of two key business processes: risk management and fraud detection. Risk management (pricing, measuring diversification, capital allocation) is all tech. Fraud detection (underwriting and claims management) is close to zero tech. When I think about what changes are likely to hit our business, risk management is where the incremental change is already happening and will continue to grind away. Startups might try their hand at this but incumbents are all over it already.

It’s in fraud and moral hazard detection that the potential for real disruption exists.

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Kinds of Stupid

From the department of unintended consequences:

We show the first evidence that one of the traits capturing childhood misbehavior, discussed in psychological literature as the externalizing trait (and linked, for example, to aggression), does indeed reduce educational attainment, but also increases earnings. This finding highlights a broader point: non-cognition is not well summarized as a single underlying trait that is either good or bad per se.

Not being good at school can be good for earnings.

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Confidence

Here’s a good friend of mine on confidence:

In a perfect world, ability and confidence would go hand-in-hand. Anyone who has managed people, however, knows that this is far from a universal truth. There is a whole body of literature on the ‘imposter syndrome’ that plagues even the most highly skilled among us, and no less than Roger Federer recently confessed to Sports Illustrated that he suffered a loss of confidence last year that lead to below average performances for him. This is something we see all the time in our own work with elite athletes and coaches.

I wonder about the channel that leads from confidence to performance. In sports, I think it’s about the athlete staying in a state of flow even while taking big competitive risks.

Business is much slower, though. The stakes can still be large but the increments are small. It’s more like practice than a game. And in this environment I praise skepticism as a starting place for confidence.

Skepticism of data. Skepticism of intuition. Many people have gotten very rich in insurance by exploiting others’ ignorance through moral hazard. It’s a messy business. And what’s more, we sell very complicated products where it’s more or less impossible to understand what a customer wants before they buy something.

If I’m training people, I teach them first to question everything. Then I teach them the tools to rebuild their understanding of the business. When you use a common set of tools, facts can be proven and confidence can be earned. When you have sold something to someone, you can say, with confidence, that “this is valuable”.

In this framing, confidence is a by-product of knowledge. If the sky is blue, it is blue, but look up first to make sure. With a culture of skepticism, we are insulated from impostors.

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Time Favors the Disruptors

the rents earned by those companies stem from their preexisting intellectual property, rather than from their current managerial talents.

That’s Tyler Cowen discussing stock buybacks. Another way of saying that big companies are slow and built to maximize what they have rather than building what they’ll need.

It’s why I enjoy being the little guy. So much opportunity!

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