Tag Archives: CAT

The Story Behind the Lemonade Hype

I am a sucker for new stuff. I bet many of you are, as well. If news of the iPhone 7’s release date caused you to immediately organize your camping gear for a week-long sidewalk holiday at your local Apple store, then you know what I am talking about. Beyond our excitement for the next iPhone or Tesla, apparently we also get all giddy for new insurance, as well.

Recently, an insurer named Lemonade has popped up on the scene and has caused quite a ripple. Here are some recent news headlines:

Wow! Give that publicist a raise. That is some quality publicity.

But it was when I saw this headline, “The Sheer Genius of Lemonade – A Whole New Paradigm for Personal Lines Insurance,” on InsNerds that I knew I had to speak out. Next thing I know, my good friend Tony Canas at InsNerds convinced me to write this response.

To start, this article is NOT a criticism of Lemonade or what it is trying to bring to the consumer. Insurance is in desperate need of heart and soul. No, what this article will do is splash some cold water on the hype inferno that appears to have taken over the sane minds of our industry. Allow me to go point-by-point with my issues:

Is Lemonade really peer-to-peer insurance?

Whether it is called peer-to-peer — or fashionably referred to as P2P — Lemonade ain’t it. Lemonade is a standard insurance company. You pay premiums, and the company pays claims from the general pool of funds. There are no peer groups insuring one another. There is no distribution model of peer invitations or referrals. The only “peer” element of the business model is that you will, as a customer, be grouped with others like you for the sole purpose of dispersing any underwriting profits to a charity of the group’s choosing. Now, there is a reason for this, but, seriously, was anything I just described even remotely connotative of peer-to-peer? Want to know what peer-to-peer looks like, see Friendsurance or Guevara.

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Is Lemonade really insurtech?

Sure, Lemonade is an online-only firm. And, yes, you can buy its insurance products through an app on your phone, where a bot named Maya will help you with your coverage selections, but Lemonade is still just an insurance company with a fancy website. I can buy insurance from other insurance companies where I can choose from dealing with a website, walking into an agent’s office or calling an agent over the phone. Lemonade has eliminated two options and given me a sole option that is little different from what I could have had before. And before you start screaming, “But I don’t want to call anyone or drive to any office,” just keep in mind that having options makes the experience better. Insurance is complicated enough that, occasionally, I would like to call someone or walk into an office and scream my head off. I deserve that option!

See also: Could an Incumbent Act Like Lemonade?  

What about the bot and the machine language? Isn’t that technology? It is technology in the sense that there are computer scientists engineering a robot to replace a human. But if the experience is crummier than just dealing with a human, it is a wasted effort.

In an attempt to play fair, I will reverse my position on this one — if it can be shown that the robot can handle the firestorm that comes when the company is hit with its first major natural catastrophe.

But isn’t it awesome that Lemonade’s underwriting profits go to charity?

One of the big marketing ideas coming from Lemonade is the unique feature of aligning the interests of policyholders and the insurer by taking excess profits and donating them to charity in the name of the peer group. Fraud is a big deal in insurance, and most insurers have systems in place to detect and counteract fraud. The charity angle from Lemonade is an attempt to prevent fraud from happening by linking the monetary loss because of fraud not to the big-bad insurer but to a softer, more sympathetic victim. Fundamentally, if you are a Lemonade policyholder and your claim is fraudulent is any way, you are depriving some charity of much-needed funds.

It is an interesting concept, but I don’t believe it will have much of a financial punch. The first drawback is that property insurance — being exposed to natural catastrophes (CAT) — is subjected to infrequent but occasionally massive losses. What appear to be underwriting profits in the quiet years between CATs are really opportunities to strengthen your balance sheet for the inevitable hit. As Lemonade expands to other states, its inability to build surplus because of the charity and the corporate status (see below), will really hamper the company’s business model. Lemonade is now, and will fully be, reliant on reinsurance to back its entire program. That by itself is not terrible, but, with full reliance on reinsurers, the excessive profits that the company thinks it will avail itself of, in reality, just go to the reinsurer. Think about this: If the reinsurer is taking all the risk, why would Berkshire Hathaway or Lloyds of London (two of the reinsuring entities for Lemonade) not want to profit from the transaction? These excess underwriting profits will simply transfer from insurer to reinsurer. My prediction is that the charitable donations will, in most years, be nonexistent or minuscule in comparison with premiums paid.

My second issue with the charity angle is that I don’t think it will bring the alignment of interest that Lemonade expects. One reason is that, if I am correct about the excess profits not materializing, then just the intermittent scheduling of charitable givings makes the whole exercise uninteresting to the insured, in my opinion. If Lemonade can’t provide a significant charitable donation in most years, the alignment will lose its appeal simply because the policyholders won’t be able to hang their hats on it. Perhaps worse, the charity angle may lose effectiveness because Lemonade is also marketing that it pays claims “super fast.”  Super fast claims handling (which, on Lemonade’s website, the company touts as a check in minutes), invites fraud. I think there is a major conflict of the business model. If your marketing message is that you can get a claims check in a few minutes without having an adjuster or claims rep work the claim, then your message is music to those upon whom the charitable message will have no impact. An an insurance buyer and seller, I know that out of super low prices, super fast claims handling and excess profits to charities, I can only choose one of those angles. More than one seems difficult. Getting all three strikes me as impossible.

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A broker by any other name…

Lemonade is a broker by another name. Another of Lemonade’s selling points is that insurers have a conflict of interest because they make money by denying claims. Lemonade purports to have absolved itself of this conflict by not actively acting like an insurer. Here’s how:

Lemonade is actually two companies. It is a risk-bearing insurance company AND a brokerage firm. When you buy a policy from Lemonade, the 20% fee goes immediately to the brokerage firm. The remaining 80% stays with the insurer. The paper on which the insurer is based is a B-corporation, which essentially makes it a non-profit. So it is the brokerage part of the business that is the money maker. That is the entity that secured all that seed-funding. Sequoia Capital knows a thing or two about making sound investments. It doesn’t do non-profits. And once the fee from the premiums the policyholder pays gets swept into the Lemonade’s brokerage company, it will not be used to pay claims, at all… ever. It is income, free of insurance risk. If the insuring entity ever goes insolvent, all the fees will be protected.

There is nothing wrong with this. The model has already been used successfully by other insurers. But, by acting as a broker, Lemonade has shifted its risk from the risk of loss or damage of the client toward that of a trusted adviser that only has one product to sell and gets a 20% commission for selling that one product. What if its product is NOT the best choice for the client? Will Maya the bot steer the buyer elsewhere like a traditional agent would? No. How forcefully will Maya point out all the flaws and gaps of Lemonade’s ISO style homeowners policy? Will Maya give direction to the insured about the flood or earthquake policy the client really should have but can’t buy through Lemonade? Somehow, I can’t match the hype and excitement of seeing a broker selling an average product, even if it’s sold via a robot.

See also: Why I’m Betting on Lemonade  

Lastly, I want to challenge the major premise of Lemonade — that insurers make money by denying claims. As a professional in the business for 20 years, I find that this is the one selling point that Lemonade and its marketing keeps touting that upsets me the most. It upsets me because it isn’t true. In fact, I have seen the opposite. I have seen emails or communications from senior executives to staff adjusters onsite during a natural disaster that flat out instructed adjusters to move quickly, be fair and, if there is any doubt about the damage, settle IN FAVOR of the policyholder. I am not naive enough to believe insurers never play fast or loose with their claims handling, but, by and large, insurers pay their claims. In the property area in which Lemonade competes, those policies it sells are legal contracts. Many a court battle has been fought to word the contract so that claims can be settled quickly and fairly. Lemonade is implying that it will be different; it is almost implying that it won’t deny claims. Are there really claims that insurers have denied (and acknowledged via the court system) that Lemonade would not have denied? I seriously doubt it.

Look, I like new things. You like new things. Lemonade is the new thing on the 300-year-old block. But the shiny new aspects that Lemonade is bringing to the table don’t appear to be worthy of the hype, in my opinion. I give them an “A” for effort in maximizing the hype to drive attention and sales. But insurance is all about the long game. The real key performance indicators (KPIs) are retention, combined ratios and customer satisfaction. Those will take years to sort out. Is Lemonade truly in it for the customer; does it really want to revolutionize the business model; or is the exit strategy already in place?

The world is watching. I hope it succeeds.

Hurricane Joaquin: Why the Model Matters

It has been fascinating watching the progression of the forecasted path for Hurricane Joaquin — what a perfect example this is of the importance of a modern data and analytics platform!

The big news is that the hurricane is not expected to make landfall on the East Coast of the U.S., but the new forecast depends as much on analytics and big data as it does on actual changes in the storm’s path. The spotlight is now on the European Center for Medium-Range Weather Forecasts (the European model) vs. the Global Forecast System (GFS) run by the National Weather Service. The New York Times has a great article discussing the reasons for the changing forecast and, crucially, the differences between the two models.

This is an excellent lesson for insurers to see the power of modern data and analytics in action and what happens to models when they are not using the advanced capabilities available today. Fortunately, investment in analytics continues to rise, as detailed in SMA’s recent report, Maturing Technologies in Insurance. Almost three in four insurers are increasing their investment in analytics over the next three years. 48% of P&C insurers, in fact, are planning to increase their analytics investments by more than 10% annually during that time.

In recent conversation with key CAT modelers, we have learned that they are working to use their weather data and insights at a more granular level than ever before in coming releases. The advance of these CAT model tools creates opportunities for insurers in search of better predictive capabilities on weather events. An upgrade to the GFS model has been planned by the end of the year, taking advantage of soon-to-be-available computing capacity. Once it is up and running, it will be interesting to see how the upgraded GFS model compares with the current European model, especially when applied to future CAT events.

Insurers can take the continuing story of Hurricane Joaquin as a wake-up call — not only is analytics a critical area for investment, but the quality of the information and the computing capacity available have a major impact on how useful predictive modeling can be.

Rethinking the Claims Value Chain

As a claims advisor, I specialize in helping to optimize property casualty claims management operations, so I spend a lot of time thinking about claims business processes, activities, dependencies and the value chains that are commonly used to structure and refine them. Lately, I have been focusing on the claims management supply chain — the vendors who provide products and perform services that are critical inputs into the claims management and fulfillment process.

In a traditional manufacturing model, the supply chain and the value chain are typically separate and — the supply chain provides raw materials, and the value chain connects activities that transform the raw materials into something valuable to customers. In a claims service delivery model, the value chain and the supply chain are increasingly overlapping, to the point where it is becoming hard to argue that any component of the claims value chain couldn’t be handled directly by the supply chain network.

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Which creates an intriguing possibility for an insurance company — an alternative to bricks and mortar and company cars and salaries, a virtual claims operation! Of course, there are third-party administrators (TPAs) that are large and well-developed enough to offer complete, end-to-end claims management and fulfillment services to an insurance company through an outsourced arrangement. That would be the one-stop shopping solution: hiring a TPA to replace your claims operation. But try to envision an end-to-end process in which you invite vendors/partners/service providers to compete to handle each component in your claims value chain (including processing handoffs to each other.) You select the best, negotiate attractive rates, lock in service guarantees and manage the whole process simply by monitoring a performance dashboard that displays real time data on effectiveness, efficiency, data quality, regulatory compliance and customer satisfaction.

You would need a system to integrate the inputs from the different suppliers to feed the dashboard, and you would also need to make certain the suppliers all worked together well enough to provide the ultimate customer with a seamless, pain free experience, but you are probably already doing some of that if you use vendors. You would still want to do quality and compliance and leakage audits, of course, but you could always hire a different vendor to do that for you or keep a small team to do it yourself.

Your unallocated loss adjustment expenses (ULAE) would become variable, tied directly to claim volume, and your main operating challenge would be to manage your supply/value chain to produce the most desirable cost and experience outcomes. Improved cycle time, efficiency, effectiveness, data accuracy and the quality of the customer experience would be your value propositions. You could even monitor the dashboard from your beach house or boat — no more staff meetings, performance reviews, training sessions — and intervene only when needed in response to pre-defined operational exceptions.

Sounds like a no-brainer. Insurance companies have been outsourcing portions of their value chain to vendors for years, so why haven’t they made their claims operations virtual?

If you are running an insurance company claims operation, you probably know why. Many (probably most) claims executives are proud of and comfortable with their claims operations just the way they are. They believe they are performing their value chain processes more effectively than anyone else could, or that their processes are “core” (so critical or so closely related to their value proposition they cannot be performed by anyone else) and thus sacrosanct, or that they have already achieved an optimal balance between in-house and outsourced services so they don’t need to push it any further. Others don’t like the loss of control associated with outsourcing, or they don’t want to consider disruptive change. Still others think it might be worth exploring, but they don’t believe they can make a successful business case for the investment in systems and change costs. Unfortunately, this may help explain why claims executives are often accused of being stubbornly change averse and overly comfortable with the status quo, but I think it is a bit more complicated than that — it all begins with the figurative “goggles” we use to self-evaluate claims operations.

If you are running a claims operation, you have an entire collection of evaluation goggles — the more claims experience you have, the larger your collection. When you have your “experience” goggles on, you compare your operation to others you have read about, or seen in prior jobs, or at competitors, to make sure your activities and results benchmark well and that you are staying up to date with best practices. At least once a year, someone outside of claims probably demands that you put your “budget” goggles on o look for opportunities to reduce ULAE costs. or legal costs, or fines and penalties, or whatever. You probably look through your “customer satisfaction” goggles quite a bit, particularly when complaints are up, or you are getting bad press because of your CAT response, or a satisfaction survey has come out and you don’t look good. Your “stakeholder” goggles help you assess how successful you have been at identifying those who have a vested interest in how well you perform, determining what it is they need from you to succeed, and delivering it. You use your “legal and regulatory compliance” goggles to identify problems before they turn into fines, bad publicity or litigation, much as you use your “no surprises” goggles to continually scan for operational breakdowns that might cause reputational or financial pain, finger pointing and second guessing. Then there are the goggles for “management” — litigation, disability, medical, vendor — and for “fraud mitigation” and “recovery” and “employee engagement.” Let’s not forget the “efficiency” goggles, which help you assess unit costs and productivity, and the “effectiveness” and “quality control” goggles, which permit you to see whether your processes are producing intended and expected results. And of course your “loss cost management” goggles give you a good read on how well you are managing all three components of your loss cost triangle, i.e., whether you are deploying and incurring the most effective combination of allocated and unallocated expenses to produce the most appropriate level of loss payments.

Are all those goggles necessary? You bet. Claims management involves complex processes and inputs and a convoluted web of variables and dependencies and contingencies. Most claims executives would probably agree it makes sense to regularly evaluate a claims operation from many different angles to get a good read on what’s working well , what isn’t and where there is opportunity for improvement. The multiple perspectives provided by your goggles help you triangulate causes, understand dependencies and impacts and intelligently balance operations to produce the best outcomes. So even if you do have a strong bias that your organization design is world-class, your people are the best and all processes and outcomes are optimal, the evaluation should give you plenty of evidence-based information with which to test that bias and identify enhancement opportunities — as long as you keep an open mind.

No matter what you do, however, there will always be others in your organization who enjoy evaluating your claims operation, and they usually aren’t encumbered by such an extensive collection of goggles. They may have only one set that is tuned to budget, or customer experience, or compliance, or they may be under the influence of consultants whose expensive goggles are tuned to detect opportunities for large-scale disruptive/destructive process innovation or transformation in your operation. On the basis of that narrow view, they just might conclude that things need to change, that new operating models need to be explored. Whether you agree or disagree, your evidence-based information should be of some value in framing and joining the debate.

Will we ever see virtual claims operations? Sure. There are many specialized claims service providers operating in the marketplace right now that can perform claims value chain processes faster, cheaper and better than many insurance companies can perform them. The technology exists to integrate multiple provider data inputs and create a performance dashboard. And there are a few large insurance company claims organizations pursuing this angle vigorously right now. I fully expect the companies that rethink and retool their claims value chains to take full advantage of integration of supply chain capabilities and begin to generate improved performance metrics and claim outcomes, ultimately creating competitive advantage for themselves. Does that mean it is time for you to rethink your claims value chain? I think the best way to find out is to put on your “innovation” goggles and take a look!

Model Errors in Disaster Planning

“All models are wrong; some are useful.” – George Box

We have spent three articles (article 1, article 2, article 3) explaining how catastrophe models provide a tool for much-needed innovation to the global insurance industry. Catastrophe models have covered for the lack of experience with many losses and let insurers properly price and underwrite risks, manage portfolios, allocate capital and design risk management strategies. Yet for all the practical benefits CAT models have infused into the industry, product innovation has stalled.

The halt in progress is a function of what models are and how they work. In fairness to those who do not put as much stock in the models as a useful tool, it is important to speak of the models’ limitations and where the next wave of innovation needs to come from.

Model Design

Models are sets of simplistic instructions that are used to explain phenomena and provide relevant insight on future events (for CAT models – estimating future catastrophic losses). We humans start using models at very early ages. No one would confuse a model airplane with a real one; however, if a parent wanted to simplify the laws of physics to explain to a child how planes fly, then a model airplane is a better tool than, say, a physics book or computer-aided design software. Conversely, if you are a college student studying engineering or aerodynamics, the reverse is true. In each case, we are attempting to use a tool – models of flight, in this instance – to explain how things work and to lend insight into what could happen based on historical data so that we can merge theory and practice into something useful. It is the constant iteration between theory and practice that allows an airplane manufacturer to build a new fighter jet, for instance. No manufacturer would foolishly build an airplane based on models no matter how scientifically advanced those models are, but those models would be incredibly useful in guiding the designers to experimental prototypes. We build models, test them, update them with new knowledge, test them again and repeat the process until we achieve desired results.

The design and use of CAT models follows this exact pattern. The first CAT models estimated loss by first calculating total industry losses and then proportionally allocating losses to insurers based on assumption of market share. That evolved into calculating loss estimates for specific locations at specific addresses. As technology advanced into the 1990s, model developers harnessed that computing power and were able to develop simulation programs to analyze more data, faster. The model vendors then added more models to cover more global peril regions. Today’s CAT models can even estimate construction type, height and building age if an insurer does not readily have that information.

As catastrophic events occur, modelers routinely compare the actual event losses with the models and measure how well or how poorly the models performed. Using actual incurred loss data helps calibrate the models and also enables modelers to better understand the areas in which improvements must be made to make them more resilient.

However, for all the effort and resources put into improving the models (model vendors spend millions of dollars each year on model research, development, improvement and quality assurance), there is still much work to be done to make them even more useful to the industry. In fact, virtually every model component has its limitations. A CAT model’s hazard module is a good example.

The hazard module takes into account the frequency and severity of potential disasters. Following the calamitous 2004 and 2005 U.S. hurricane seasons, the chief model vendors felt pressure to amend their base catalogs with something to reflect the new high-risk era we were in, that is, taking into account higher-than-average sea surface temperatures. These model changes dramatically affected reinsurance purchase decisions and account pricing. And yet, little followed. What was assumed to be the new normal of risk taking actually turned into one of the quietest periods on record.

Another example was the magnitude 9.0, 2011 Great Tōhuko Earthquake in Japan. The models had no events even close to this monster earthquake in their event catalogs. Every model clearly got it wrong, and, as a result, model vendors scrambled to fix this “error” in the model. Have the errors been corrected? Perhaps in these circumstances, but what other significant model errors exist that have yet to be corrected?

CAT model peer reviewers have also taken issue with actual event catalogs used in the modeling process to quantify catastrophic loss. For example, a problem for insurers is answering the type of question of: What is the probability of a Category 5 hurricane making landfall in New York City? Of course, no one can provide an answer with certainty. However, while no one can doubt the significance of the level of damage an event of that intensity would bring to New York City (Super Storm Sandy was not even a hurricane at landfall in 2012 and yet caused tens of billions of dollars in insured damages), the critical question for insurers is: Is this event rare enough that it can be ignored, or do we need to prepare for an event of that magnitude?

To place this into context, the Category 3, 1938 Long Island Express event would probably cause more than $50 billion in insured losses today, and that event did not even strike New York City. If a Category 5 hurricane hitting New York City was estimated to cause $100 billion in insured losses, then knowing whether this was a 1-in-10,000-year possibility or a 1-in-100-year possibility could mean the difference between solvency and insolvency for many carriers. If that type of storm was closer to a 1-in-100-year probability, then insurers have the obligation to manage their operations around this possibility; the consequences are too grave, otherwise.

Taking into account the various chances of a Category 5 directly striking New York City, what does that all mean? It means that adjustments in underwriting, pricing, accumulated capacity in that region and, of course, reinsurance design all need to be considered — or reconsidered, depending on an insurer’s present position relative to its risk appetite. Knowing the true probability is not possible at this time; we need more time and research to understand that. Unfortunately for insurers, rating agencies and regulators, we live in the present, and sole reliance on the models to provide “answers” is not enough.

Compounding this problem is that, regardless of the peril, errors exist in every model’s event catalog. These errors cannot even be avoided, and the problem escalates where our paucity of historical recordings and scientific experiments limit our industry’s ability to inch us closer and closer to greater certainty.

Earthquake models still lie beyond a comfortable reach of predictability. Some of the largest and most consequential earthquakes in U.S. history have occurred near New Madrid, MO. Scientists are still wrestling with the mechanics of that fault system. Thus, managing a portfolio of properties solely dependent on CAT model output is foolhardy at best. There is too much financial consequence from phenomena that scientists still do not understand.

Modelers also need to continuously assess property vulnerability when it comes to taking into consideration various building stock types with current building codes. Assessing this with imperfect data and across differing building codes and regulations is difficult. That is largely the reason that so-called “vulnerability curves” oftentimes are revised after spates of significant events. Understandably, each event yields additional data points for consideration, which must be taken into account in future model versions. Damage surveys following Hurricane Ike showed that the models underestimated contents vulnerability within large high-rises because of water damage caused by wind-driven rain.

As previously described, a model is a set of simplified instructions, which can be programmed to make various assumptions based on the input provided. Models, therefore, fall into the Garbage In – Garbage out complex. As insurers adapt to these new models, they often need to cajole their legacy IT systems to provide the required data to run the models. For many insurers, this is an expensive and resource-intensive process, often taking years.

Data Quality’s Importance

Currently, the quality of industry data to be used in such tools as CAT models is generally considered poor. Many insurers are inputting unchecked data into the models. For example, it is not uncommon that building construction type, occupancy, height and age, not to mention a property’s actual physical address, are unknown! For each  property whose primary and secondary risk characteristics are missing, the models must make assumptions regarding those precious missing inputs – even regarding where the property is located. This increases model uncertainty, which can lead to inaccurate assessment of an insurer’s risk exposure.

CAT modeling results are largely ineffective without quality data collection. For insurers, the key risk is that poor data quality could lead to a misunderstanding regarding what their exposure is to potential catastrophic events. This, in turn, will have an impact on portfolio management, possibly leading to unwanted exposure distribution and unexpected losses, which will affect both insurers’ and their reinsurers’ balance sheets. If model results are skewed as a result of poor data quality, this can lead to incorrect assumptions, inadequate capitalization and the failure to purchase sufficient reinsurance for insurers. Model results based on complete and accurate data ensures greater model output certainty and credibility.

The Future

Models are designed and built based on information from the past. Using them is like trying to drive a car by only looking in the rear view mirror; nonetheless, catastrophes, whether natural or man-made, are inevitable, and having a robust means to quantify them is critical to the global insurance marketplace and lifecycle.

Or is it?

Models, and CAT models in particular, provide a credible industry tool to simulate the future based on the past, but is it possible to simulate the future based on perceived trends and worst-case scenarios? Every CAT model has its imperfections, which must be taken into account, especially when employing modeling best practices. All key stakeholders in the global insurance market, from retail and wholesale brokers to reinsurance intermediaries, from insurers to reinsurers and to the capital markets and beyond, must understand the extent of those imperfections, how error-sensitive the models can be and how those imperfections must be accounted for to gain the most accurate insight into individual risks or entire risk portfolios. The difference in a few can mean a lot.

The next wave of innovation in property insurance will come from going back to insurance basics: managing risk for the customer. Despite model limitations, creative and innovative entrepreneurs will use models to bundle complex packages of risks that will be both profitable to the insurer and economical to the consumer. Consumers desiring to protect themselves from earthquake risks in California, hurricane risks in Florida and flood risks on the coast and inland will have more options. Insurers looking to deploy capital and find new avenues of growth will use CAT models to simulate millions of scenarios to custom create portfolios optimizing their capacity and create innovative product features to distinguish their products against competitors. Intermediaries will use the models to educate and craft effective risk management programs to maximize their clients’ profitability.

For all the benefit CAT models have provided the industry over the past 25 years, we are only driving the benefit down to the consumer in marginal ways. The successful property insurers of the future will be the ones who close the circle and use the models to create products that make the transfer of earthquake, hurricane and other catastrophic risks available and affordable.

In our next article, we will examine how we can use CAT models to solve some of the critical insurance problems we face.