Insurers (finally) Starting to Ape Buffett

David Merkel reviews Buffett’s 10k:

Buffett does very well, but I know of no other insurer that invests so much in equities funded by insurance liabilities.  There is a real risk that if the markets fall hard, a la 1929-32, 1973-4, 2007-8. that BRK would be hard-pressed, particularly if there were some significant disaster like Katrina or Sandy, or set of disasters like 2004 or 2011.

He’s right that this philosophy adds risk to the business model. But riding equities isn’t as lonely a strategy for Buffett as it once was.

Consider Greenlight Re, an offshore reinsurance company linked to a hedge fund. Their strategy departed from the typical offshore startup model: write long tail, low-volatility, high-float insurance business and pump the cash into the hedge fund.

Taking big investment risks isn’t new for insurers. It’s just that doing it on purpose is unorthodox. Many insurers quietly took on more asset risk in the mid-00s and got crushed in 08. So did Greenlight for that matter. The difference is that Greenlight, fully expecting such a scenario, kept a hand steady at the till and rode the market back up.

Others tucked their tail between their legs and liquidated, in many cases locking in the losses.

If everyone (regulator, rating agency, management, investors) is on board with the risk, aggressive asset investment coupled with stable, high-float liabilities can work. Put another way, linking a hedge fund to an insurance company means the insurer can get by at lower combined ratios and grow.

But don’t take my word for it, look to the market and see the latest wave of offshore startups copying Greenlight/BRK: Third Point Re, SAC Re. And we see this convergence coming from the other side as AWAC, an established (re)insurer, bought into a hedge fund.

The key is to think of these insurers as more akin to banks: low-risk liabilities, high-risk assets. One can be skeptical of whether they are doing a good job of balancing these risks but in principle there is no reason to assume they will fail.

What About Micro Insurance?

To me, the big reason why insurance has resisted wholesale disruption is that the problem being solved isn’t the cost of the product. In a certain kind of universe, insurance has virtually zero cost: it’s the sum of expected claims plus some compensation for caretakers of a giant bond portfolio. In the real world this amounts to something like 70% of the cost of your insurance policy.

The rest is all about people trying to f#*& each other over. Insurers are trying to charge the highest price and policyholders are trying to get someone else to pay for their mistakes.

Now you need to introduce underwriters and agents/brokers. Underwriters basically get paid to sniff out moral hazard and agents get paid to force insurers to compete. Together these two groups form about 30% of the cost of your insurance policy.

But here’s the problem: only humans can figure out if humans are trying to f#$% someone over. It’s immune to automation. For now, anyway.

This is why mutuals are so popular. They’re, in principle, owned by policyholders* who, by definition aren’t going to f*^% themselves over. That’s basically the idea of micro-finance. Lend some money to one person in a group. Then lend to another. If one person defaults, everyone defaults and the well runs dry.

Peer pressure is the most powerful force in humanity.

*In practice, mutuals tend to get really big and agency theory sets in. Management’s interests diverge from policyholders’ as oversight gets more costly. One striking thing about mutuals is that they’re often run like family businesses. Multi-generation CEOships and the like.

Technology Giveth And Taketh Away

Independent Insurance agents are again on the rise. See below for a quote.

The story goes that technology forced consolidations in the 90s. Brokers needed scale and capital for SYSTEMS and batteries of tech priests to keep server rooms humming.

Today Automation is shaving all that infrastructure down to a rump. For many, an iPad takes care of 95% of what they need to do at work. The rest are either analysts, for whom a golden age of data has dawned, or middle-skilled system jockeys. The latter always glancing over their shoulders at aforementioned, all-consuming Automation.

Schumpeter, the job eating furnace: “Chomp, Chomp, Chomp, Chomp, Chomp, Chomp”

Key findings of the study, which is is sponsored by Future One, a collaboration of the Independent Insurance Agents & Brokers of America (the Big “I”) and independent agency companies, include:

  • The number of independent agencies has grown. After declining from 44,000 in 1996 to 37,500 in 2006, the number of independent agencies has grown to 38,500 in the past two years.
  • Business conditions for independent agencies improved between the 2010 and 2012 studies. In 2012, 60 percent reported increased revenue, compared to 42 percent in the 2010 study.
  •  Systems and data security are now the most important technology challenges facing agencies.
  • Agencies are beginning to use the Internet more to obtain new customers. About 25 percent use Facebook to keep in touch with prospects, and 20 percent use LinkedIn.

Hat tip

What Is A Good Company? A Bad One?

In good organizations, people can focus on their work and have confidence that if they get their work done, good things will happen for both the company and them personally. It is a true pleasure to work in an organization such as this. Every person can wake up knowing that the work they do will be efficient, effective and make a difference both for the organization and themselves. These things make their jobs both motivating and fulfilling.

In a poor organization, on the other hand, people spend much of their time fighting organizational boundaries, infighting and broken processes. They are not even clear on what their jobs are, so there is no way to know if they are getting the job done or not. In the miracle case that they work ridiculous hours and get the job done, they have no idea what it means for the company or their careers…

That’s Ben Horowitz. There is also a discussion about a company called Go, which you’ve never heard of but are about to learn something from:

When I first met my friend Bill Campbell, he was chairman of Intuit, on the board of Apple and a mentor to many of the top CEOs in the industry, including Steve Jobs and Jeff Bezos. However, those things did not impress me nearly as much as his time running a company called GO Corporation. GO essentially attempted to build an iPhone in 1992. The company raised more money than almost any other venture capital back startup in history and lost nearly all of it before selling itself for nearly nothing to AT&T in 1994.

Now that probably doesn’t sound impressive. In fact, it probably sounds like a horrible failure. But I’d met tens of GO employees in my career, including great people like Mike Homer, Danny Shader, Frank Chen and Stratton Sclavous, and the amazing thing was that every GO employee that I’d ever met counted GO as one of the greatest work experiences of their lives. The best work experience ever despite the fact that their careers stood still, they made no money and they were front-page failures. GO was a good place to work.

My favorite test for whether a company is excellent company or not is whether the people who were a part of it go on to do extraordinary things. Think of the Paypal mafia.  It’s not clear to me that GO passes this test.

In the (re)insurance business, there are two Paypal mafias that come to mind: AIG’s actuarial department in the 80s and F&G Re. The top ranks of my business have over the last 20 years been massively over-represented by people with one of these two lines on their resume.

There is also an interesting discussion on HN about a rather controversial passage in the piece. Here’s the setup:

At Opsware I used to teach a management expectations course because I deeply believed in training. In it, I made it clear that I expected every manager to meet with her people on a regular basis. I even gave instructions on how to conduct a 1:1 meeting so there could be no excuses.

Then one day while I happily went about my job, it came to my attention that one of my managers hadn’t had a 1:1 with any of his employees in over six months.

Ben threatened to fire this manager and the manager’s boss if they didn’t fix this in 24 hours. A commenter thought that was harsh and perhaps a bit arbitrary. Another one responded with this:

It’s even worse than that, the people that work for you will make their number one priority not getting fired.

I’ve worked at a company like that before. Management worked hard on whatever problem the CEO noticed last, while doing their best to hide any other problems from him.
As a manager you do much better at aligning everyone’s interests so that your staff does what they want to do, which just happens to work towards the outcome you want. It’s more about gentle course corrections ahead of time than grabbing the wheel from them.

This is evocative to me of the hardest management problem in insurance. In insurance, like in all businesses, there is pressure for companies to grow. In insurance, also like in other businesses, growing by cutting your prices to the point where you lose money is one self-defeating option.

Unlike in other businesses, though, the break-even price for insurance is basically unknowable when you charge it. So you charge something that is probably close and work out differences across time.

This means insurance managers can be a schizophrenic bunch. One day they’re focused on growth, the next day they’re worried that they’re cutting their prices too much to grow profitably. The worst managers expose their employees to the full horror of this uncertainty. The best find a balance and help their subordinates find their own balance.

I’m not sure this criticism is justified in the story, but the lesson still stands: understand your management priorities and be consistent in applying them.

And one of those priorities is that your turf should be a good place to work.

Banks And Insurers: Full of Fail?

This is a neat little paper: How Complex Systems Fail. It is short, it is simple and it is absolutely PACKED with insight. Here is are some excerpts:

5. Complex systems run in degraded mode.
A corollary to the preceding point is that complex systems run as broken systems. The system continues to function because it contains so many redundancies and because people can make it function, despite the presence of many flaws. After accident reviews nearly always note that the system has a history of prior ‘proto-accidents’ that nearly generated catastrophe. Arguments that these degraded conditions should have been recognized before the overt accident are usually predicated on naïve notions of system performance. System operations are dynamic, with components (organizational, human, technical) failing and being replaced continuously

7. Post-accident attribution accident to a ‘root cause’ is fundamentally wrong.
Because overt failure requires multiple faults, there is no isolated ‘cause’ of an accident… The evaluations based on such reasoning as ‘root cause’ do not reflect a technical understanding of the nature of failure but rather the social, cultural need to blame specific, localized forces or events for outcomes.

One thing that strikes me about the paper is that the author (probably deliberately) does not try to define what a complex system is. In a sense the paper is a definition of a complex system, which is to say that they are defined by how they fail. Or, perhaps like with pornography: you know it when you see it.

I can see two ways that a complex system can develop and operate: top down or bottom up. Bottom up systems get to be much much more complex, yet I would say that they are much less prone to failure. Perhaps that last sentence is saying the same thing twice.

I think of this in terms of risk management at insurance companies or banks. You can imagine that a weak grasp of how systems fail could be financially ruinous: for example, by an executive believing he/she has a better grasp for the ‘root cause’ of why failures occur.

To run a complex system perhaps requires humility in the face of something you simply cannot understand.

The Market is Smarter Than You

Two pretty different links here:

1. Scott Sumner is really starting to get good at nailing down his view. It’s been fun watching his writing sharpen up over the last few years. Nobody who cares about macroeconomics can afford to ignore his blog:

In recent months central banks have resorted to using the phony “credibility” issue.  The claim is that they had to fight hard in the 1970s and early 1980s to get markets to believe they were serious about inflation.

Fortunately, that is simply not true.   Markets have little difficulty figuring out what central banks are up to.  When the central bank wants to reduce inflation (as they did after 1981) markets believe them.  When they didn’t want to, markets didn’t believe they’d lower inflation.  There never was a credibility problem.

…I’ve frequently argued that interest rate targeting is like a car with a  steering wheel that locks when you need it most–on twisty mountain roads with no guardrail.  I’ve also argued that although we rarely hit the zero rate bound in past recessions, it may well become the norm in future recessions.

2. David Merkel (who holds the opposite macro philosophy to Sumner, incidentally) with a couple good stories. I like this comment:

rapid growth in financial institutions is rarely a good thing; it usually means that an error has been made.  Two, there is a barrier in many financial decisions, where responsible parties are loath to cry foul until it is way past obvious, because the cost of being wrong is high.

Insurance companies are excellent long term investments if they’re boring. They’re boring because they can’t really grow quickly, because if they do they will die.

The smartest P&C (Re)insurers I know of have a very simple strategy: sit on your hands for about 75% of your career and pray your investors don’t fire you for it. When the market turns step on the gas. Repeat.

The effectiveness of that strategy is proportional to how much you are in the business of assuming insurance risk. Think of the insurance world as a spectrum from support businesses (IT vendors, auditors, etc) which are more or less acyclical to Reinsurers which are completely beholden to the cycle.

From least to most insurance risk:

IT vendor -> broker -> MGA -> Berkshire Hathaway ->  insurer -> Reinsurers

BRK gets its own spot because  it’s an interesting mix of ‘normal’ businesses and insurance businesses. It’s the ability to put their capital to work in something that doesn’t care about the soft market that makes them unique. They DO something with that 75% of their time.

When the market is hard, insurance is an excellent business to be in: entrance is difficult, and the mass extinction of the turn scares the bejesus out of less skilled underwriters. Finding a strategy that lets you capitalize on that but doesn’t handcuff you to soft market valuations is a big deal.

Reverse Engineers

Leaky, a car insurance comparison website, ran into a problem:

The problem? In order to compare the insurance prices you’d pay with different providers, Leaky was scraping the data directly from the insurance companies’ websites. It sounds like Traff wasn’t entirely surprised by the letters (“We understood their objections and complied with them,” he says now), but he thought Leaky would have more time to fly under-the-radar while it figured out the best way to get its data. However, the high-profile launch made that impossible, and the site went offline after four days.

The solution?

Now Leaky is back, and it’s offering price comparisons based on a new data source — the regulatory filings that car insurance companies have to file with the government. Using those filings, the company has created a model that predicts, based on your personal details, how much each insurance provider will charge.

I presume he means the rate filings insurers give to regulators (I smell an actuary in there somewhere!). This is a fascinating project but I’m pretty pessimistic.

The web startup model, as I see it, is to build something geeks love, piggyback on the free advertising in the startup press and wait to get bought out by someone who has the platform to actually bring your product to the masses.

Leaky is offering no product, though. They’re offering replica pricing. Oh, but it’s so close to the real thing!

That means Leaky is no longer getting its prices directly from the providers, but Traff says the new model is making predictions that fall within 3 percent of the actual prices.

First lesson in stats: means mask the tails of the distribution. There’s plenty of wiggle room in 3% average deviation (if that’s what he means) to make this product completely useless.

Car insurance is not unlike car manufacturing. I remember reading an interview with Carlos Ghosn where he was lamenting that the only way to make money is to have huge scale in auto manufacturing and the only way to get that scale is to kill your margins.

Online platforms, like manufacturing plants, are a colossal capital outlay. As soon as it’s up insurers need to pour money into advertising to get people to the site. Sure you’re cutting out the broker, but you need to pay Google and network TV to get the word out and promise (cross our hearts) that your deals are actually cheaper.

And the real cheap deals only come occasionally as a carrier grasps for market share. Leaky can’t predict that from the rate filing.

So the only way to improve on the existing model is to compare real quotes from real insurers. Online players killed the broker a long time ago, they aren’t going to let him back in now.

Flummoxed By Florida No Fault

There’s change a-happening in the Florida auto insurance market.

Auto insurance is expensive for Floridians. The reason is that they file a lot of expensive claims, more than most. Floridians do this because… well that’s what I’ve been thinking a lot about lately.

First I’ll misquote Bastiat:

Claims Fraud is the great fiction through which everybody endeavors to live at the expense of everybody else.

Ok, let’s swipe some graphs from the indispensable III to illustrate the problem (source here and here).

Exhibit A:

So there’s a problem with auto insurance. Got it. Why?

Well, Florida is a No-Fault state, which means that beneath a certain threshold ($10,000 in this case) you claim on your own insurance policy when you get in an accident regardless of who hit whom. Everyone is pretty focused on the No-Fault aspect of the problem.

And there’s evidence of a problem. Here’s a graph detailing the growth in claims frequency and severity for No-Fault in Florida:

And newspapers have been going bananas down in FL, decrying the Florida No-Fault “Fraud Tax”. Catchy, non?

I’m not completely sure what a “Fraud Tax” is (I haven’t found any published methodology for calculating it anywhere), but here is the III’s view:

The combined impact of rising frequency and severity of claims is driving up the cost of pure premium, which is defined as the premium needed to pay for anticipated losses without considering other costs of doing business. The only reasonable explanation for this dramatic rise: no-fault fraud and abuse.

Even given that you accept that claims frequency and severity are increasing in Florida, I’d say that’s a pretty weak assertion.

They get stronger as the report continues.

Insurers also report suspected fraud to the National Insurance Crime Bureau (NICB), an insurer-funded, nonprofit organization of more than 1,000 members, including property/casualty insurers. The NICB is the nation’s leading organization dedicated to preventing, detecting and defeating insurance fraud and vehicle theft. The NICB gives a closer review to claims that are considered questionable and investigates them based on one or more indicators of possible fraud. A single auto insurance claim may be referred to the NICB for several reasons, and these “questionable claims” are flagged because they possess indicators of:

  1. Staged accidents
  2. Excessive medical treatment
  3. Faked or exaggerated injury
  4. Prior injuries (that are unreported in the new claim) Insurance Information Institute
  5. Bills for service not rendered
  6. Solicitation of the accident victim(s)

A single claim may contain several referral reasons. Questionable claims involving staged accidents surged 52 percent in 2009. For 2010, early estimates suggest an even larger increase.

And the kicker is this graph:

No fault is increasing quite a lot. But EVERYTHING is increasing, isn’t it? And how about #2, there, Bodily Injury? Well Bodily Injury is actually where the story is, in my mind. That’s the At-Fault coverage that extends above the $10,000 cap on No-Fault. You need to go to court and sue people and stuff for that.

What’s more, the Bodily Injury insurance market is 2.5x the size of No-Fault in Florida. BI is the 800 pound gorilla. Why isn’t anyone talking about it if it’s in the dumps, too?

Chris Tidball has an interesting analysis (and is now on the blogroll!):

The problem in Florida is substantial. First, the threshold for determining whether a party may sue has been watered down by the courts over the years, meaning that virtually any injury, irrespective of how minor it actually is, can be adjudicated, even if the true interpretation of the tort threshold says otherwise.

Secondly, a person is able to sue for any percentage of damage for which they were not at fault. Even if a person is 99.9 percent at fault, they are able to sue for damages.

None of that is No-Fault and I’m not sure how it’s related to the organized staged accidents and No-Fault Fraud. Are we addressing the wrong problem?

Ok, give me your hand and let’s walk slowly through this regrettably dense graph I put together with SNL data on the Florida market.

The solid lines are the written premium levels (left axis – see how much higher At-Fault is?) and the dotted lines are reported loss ratios (right axis):

Note that there is a bit of a basis mismatch in the data presented. The loss ratios are reported losses over earned premium while the premium is written premium, which is a more responsive indicator of market pricing.

The grey region is a classic market turn. Claims costs go up massively, insurers lose lots of money and premiums respond after a lag. It happened in No-Fault and it happened in At-Fault.

This time is different. No-Fault is playing that movie over again but At-Fault doesn’t seem to be, in spite of the increase in staged accidents noted above. What are we to make of this?

Some possibilities:

  1. The problem isn’t fraud, which appears to be affecting both No-Fault and At-Fault similarly without a similar impact on loss ratios;
  2. Fraud incidence is higher in At Fault but fraudsters are less successful when they need to go to court.

One observation on the graph above: In 2007, Florida’s No Fault law expired FL was At-Fault only for 3 months. But the loss ratio for that year was higher!

What’d I’d really like is to find a natural experiment in a state that modified its No-Fault laws. The only example I can see is in Colorado, which repealed its No-Fault system in 2003.

Here’s what happened:

At-Fault loss ratios dropped a bit immediately, but the drop persists!

Remember the purpose of No-Fault was to lower expenses. I’d imagine that claims expenses and overhead have also risen on the At-Fault book to deal with increase in smaller claims.

Does that mean that we should expect a higher expense ratio on the At-Fault FL book once No-Fault reform comes into play? That would suggest an advantage to carriers with efficient back offices…

P&C Stocks

David Merkel has an analysis out on insurance stocks. By the way, I deeply respect anyone willing to put original analysis out on the web, as David does frequently. That’s the stuff that drives the blogosphere.

His understanding is way broader than mine on theis business. He begins by dismissing Title and Credit insurers are no longer relevant stand-alone categories, which is great because I know nothing about them. He spends a few minutes on life and health insurers, which I also know nothing about.

I’m a P&C guy and haven’t had any training or experience in anything else. One thing Merkel didn’t get to is the P&C cycle. The best way of talking about this is through his graphs.

You can clearly see how tough it is for that group of insurers to break out past the 1.0 BV line. The market is very skeptical of the profits of those on the right side of the line.

Insurance is a cyclical business and right now we’re approaching the trough. The typical company below 1.0 BV and to the right of the line is probably only performing so well because they are releasing redundant reserves from prior years (translation: business a few years ago is proving more profitable than predicted, so offsets poor results today). Can’t go on forever.

The offshore businesses have a similar problem but the scale of the y axis obscures things a little bit. Almost all of these companies are below the 1.0 BV and the ROE band is shifted out. They’re more profitable and lower-value. Weird!

My gut feel for why this is has to do with the breakdown of business mix. Most insurance companies do two things: they originate/distribute insurance risk and they keep insurance risk. The first business is much more valuable than the second because the infrastructure of distribution is valuable.

Contrast this with reinsurers, who only hold insurance risk and probably dominate this offshore group. Their barriers to entry are super low (three guys, an office in Bermuda and a rolodex full of Reinsurance Brokers!). New entrants are kept away by the spectre of measly profits.

I was still in the reinsurance nursery for the last market turn. I’m looking forward to seeing that it’s like.

Science (?) And My Insurance BS Test

Richard Feynman defines science as the study of nature and engineering as the study of things we make. I like that logic and it makes the idea of an insurance company hiring a Chief Science Officer faintly ridiculous. Science today means ‘using tools that scientists use’.

Anyway, I have a test for the degree to which an article on insurance is BS or not. It’s the Climate Change Test. If the article or interviewee mentions climate change as a problem they want to think about in connection with insurance rates, they’re probably full of it.

My point is that big politicized science questions have no place at an underwriter’s desk: identifying claims trends is fine, but don’t dress the discussion up in some topic du jour just to pretend to be talking about something ‘people care about’. That’s pure, irritating status affiliation.

Well guess what:

MB: For the present we’ll be organized such that the operational analytics will continue to reside in the business units. On one end of a continuum is the traditional loss modeling; on the other end we’ll be responding to things like climate change in partnership with institutions such as the RAND Corporation. On a scale of one to ten, the familiar operational analytics may be a “one” and collaboration with RAND might be a 10. The sweet spot for the office is probably between four and 10. I envision that the science team will support the businesses in questions that have been asked but not addressed because of immediate burning issues or haven’t been asked in the most cohesive way.

Jim Lynch is puzzled about whether this is an actuarial role or not. It sure is. In most companies, C-suite folks all have ridiculously busy jobs so can’t focus on data mining and statistical analysis. But most companies don’t employ hundreds of highly trained statisticians to think about these problems every day. AIG does.

Anyway, what’s his strategy? Go fancy:

MB: Commercial and personal property insurance is largely about low-frequency, high-severity risk. The industry has tried with limited success is to model that risk through traditional analytic techniques. However, there remains a huge amount of volatility associated with an insurance company’s finances. We hope to explore ways of thinking about risk questions differently, approaching them from a different angle while leveraging relevant data. It’s more than a matter of using traditional and even non-traditional statistical analysis; it’s about bringing game theory, possibly real options theory and more broadly about reshaping the approach fundamentally to gain new insight into how to manage claims and better understand low-frequency, high-value events.

He’s been an internal consultant in insurance for 10 years. I’ll be surprised if he can come up with ways of out-analyzing the teams of actuaries AIG employs.

*Bad writing award for this line from his CV:

Creating and leading the team challenged to inculcate science driven decision making into an organization that has achieved great success by making heuristic decisions on the backbone of its sales force.