Tag Archives: lamparelli

How CAT Models Lead to Soft Prices

In our first article in this series, we looked back at an insurance industry reeling from several consecutive natural catastrophes that generated combined insured losses exceeding $30 billion. In the second article, we looked at how, beginning in the mid-1980s, people began developing models that could prevent recurrences of those staggering losses. In this article, we look at how modeling results are being used in the industry.

 

Insurance is a unique business. In most other businesses, expenses associated with costs of operation are either known or can be fairly estimated. The insurance industry, however, needs to estimate expenses for things that are extremely rare or have never happened before. Things such as the damage to a bridge in New York City from a flood or the theft of a precious heirloom from your home or the fire at a factory, or even Jennifer Lopez injuring her hind side. No other industry has to make so many critical business decisions as blindly as the insurance industry. Even in circumstances in which an insurer can accurately estimate a loss to a single policyholder, without the ability to accurately estimate multiple losses all occurring simultaneously, which is what happens during natural catastrophes, the insurer is still operating blindly. Fortunately, the introduction of CAT models greatly enhances both the insurer’s ability to estimate the expenses (losses) associated with a single policyholder and concurrent claims from a single occurrence.

When making decisions about which risks to insure, how much to insure them for and how much premium is required to profitably accept the risk, there are essentially two metrics that can provide the clarity needed to do the job. Whether you are a portfolio manager managing the cumulative risk for a large line of business or an underwriter getting a submission from a broker to insure a factory or an actuary responsible for pricing exposure, what these stakeholders need to minimally know is:

  1. On average, what will potential future losses look like?
  2. On average, what are the reasonable worst case loss scenarios, or the probable maximum loss (PML)?

Those two metrics alone supply enough information for an insurer to make critical business decisions in these key areas:

  • Risk selection
  • Risk-based pricing
  • Capacity allocation
  • Reinsurance program design

Risk Selection

Risk selection includes an underwriter’s determination of the class (such as preferred, standard or substandard) to which a particular risk is deemed to belong, its acceptance or rejection and (if accepted) the premium.

Consider two homes: a $1 million wood frame home and a $1 million brick home both located in Los Angeles. Which home is riskier to the insurer?  Before the advent of catastrophe models, the determination was based on historical data and, essentially, opinion. Insurers could have hired engineers who would have informed them that brick homes are much more susceptible to damage than wood frame homes under earthquake stresses. But it was not until the introduction of the models that insurers could finally quantify how much financial risk they were exposed to. They shockingly discovered that on average brick homes are four times riskier than wood frame homes and are twice as likely to sustain a complete loss (full collapse). This was data not well-known by insurers.

Knowing how two or more different risks (or groups of risks) behave at an absolute and relational level provides a foundation to insurers to intelligently set underwriting guidelines, which work toward their strengths and excludes risks they do not or cannot absorb, based on their risk appetite.

Risk-Based Pricing

Insurance is rapidly becoming more of a commodity, with customers often choosing their insurer purely on the basis of price. As a result, accurate ratemaking has become more important than ever. In fact, a Towers Perrin survey found that 96% of insurers consider sophisticated rating and pricing to be either essential or very important.

Multiple factors go into determining premium rates, and, as competition increases, insurers are introducing innovative rate structures. The critical question in ratemaking is: What risk factors or variables are important for predicting the likelihood, frequency and severity of a loss? Although there are many obvious risk factors that affect rates, subtle and non-intuitive relationships can exist among variables that are difficult, if not impossible, to identify without applying more sophisticated analyses.

Regarding our example involving the two homes situated in Los Angeles, catastrophe models tell us two very important things: what the premium to cover earthquake loss should roughly be and that the premium for masonry homes should be approximately four times larger than wood frame homes.

The concept of absolute and relational pricing using catastrophe models is revolutionary. Many in the industry may balk at our term “revolutionary,” but insurers using the models to establish appropriate price levels for property exposures have a massive advantage over public entities such as the California Earthquake Authority (CEA) and the National Flood Insurance Program (NFIP) that do not adhere to risk-based pricing.

The NFIP and CEA, like most quasi-government insurance entities, differ in their pricing from private insurers along multiple dimensions, mostly because of constraints imposed by law. Innovative insurers recognize that there are literally billions of valuable premium dollars at stake for risks for which the CEA, the NFIP and similar programs significantly overcharge – again, because of constraints that forbid them from being competitive.

Thus, using average and extreme modeled loss estimates not only ensures that insurers are managing their portfolios effectively, but enables insurers, especially those that tend to have more robust risk appetites, to identify underserved markets and seize valuable market share. From a risk perspective, a return on investment can be calculated via catastrophe models.

It is incumbent upon insurers to identify the risks they don’t wish to underwrite as well as answer such questions as: Are wood frame houses less expensive to insure than homes made of joisted masonry? and, What is the relationship between claims severity and a particular home’s loss history? Traditional univariate pricing analysis methodologies are outdated; insurers have turned to multivariate statistical pricing techniques and methodologies to best understand the relationships between multiple risk variables. With that in mind, insurers need to consider other factors, too, such as marketing costs, conversion rates and customer buying behavior, just to name a few, to accurately price risks. Gone are the days when unsophisticated pricing and risk selection methodologies were employed. Innovative insurers today cross industry lines by paying more and more attention to how others manage data and assign value to risk.

Capacity Allocation

In the (re)insurance industry, (re)insurers only accept risks if those risks are within the capacity limits they have established based on their risk appetites. “Capacity” means the maximum limit of liability offered by an insurer during a defined period. Oftentimes, especially when it comes to natural catastrophe, some risks have a much greater accumulation potential, and that accumulation potential is typically a result of dependencies between individual risks.

Take houses and automobiles. A high concentration of those exposure types may very well be affected by the same catastrophic event – whether a hurricane, severe thunderstorm, earthquake, etc. That risk concentration could potentially put a reinsurer (or insurer) in the unenviable position of being overly exposed to a catastrophic single-loss occurrence.  Having a means to adequately control exposure-to-accumulation is critical in the risk management process. Capacity allocation enables companies to allocate valuable risk capacity to specific perils within specific markets and accumulation zones to minimize their exposure, and CAT models allow insurers to measure how capacity is being used and how efficiently it is being deployed.

Reinsurance Program Design

With the advent of CAT models, insurers now have the ability to simulate different combinations of treaties and programs to find the right fit, maximizing their risk and return. Before CAT models, it would require gut instinct to estimate the probability of attachment of one layer over another or to estimate the average annual losses for a per-risk treaty covering millions of exposures. The models estimate the risk and can calculate the millions of potential claims transactions, which would be nearly impossible to do without computers and simulation.

It is now well-known how soft the current reinsurance market is. Alternative capital has been a major driving force, but we consider the maturation of CAT models as having an equally important role in this trend.

First, insurers using CAT models to underwrite, price and manage risk can now intelligently present their exposure and effectively defend their position on terms and conditions. Gone are the days when reinsurers would have the upper hand in negotiations; CAT models have leveled the playing field for insurers.

Secondly, alternative capital could not have the impact that it is currently having without the language of finance. CAT models speak that language. The models provide necessary statistics for financial firms looking to allocate capital in this area. Risk transfer becomes so much more fungible once there is common recognition of the probability of loss between transferor and transferee. No CAT models, no loss estimates. No loss estimates, no alternative capital. No alternative capital, no soft market.

A Needed Balance

By now, and for good reason, the industry has placed much of its trust in CAT models to selectively manage portfolios to minimize PML potential. Insurers and reinsurers alike need the ability to quantify and identify peak exposure areas, and the models stand ready to help understand and manage portfolios as part of a carrier’s risk management process. However, a balance between the need to bear risk and the need to preserve a carrier’s financial integrity in the face of potential catastrophic loss is essential. The idea is to pursue a blend of internal and external solutions to ensure two key factors:

  1. The ability to identify, quantify and estimate the chances of an event occurring and the extent of likely losses, and
  2. The ability to set adequate rates.

Once companies have an understanding of their catastrophe potential, they can effectively formulate underwriting guidelines to act as control valves on their catastrophe loss potential but, most importantly, even in high-risk regions, identify those exposures that still can meet underwriting criteria based on any given risk appetite. Underwriting criteria relative to writing catastrophe-prone exposure must be used as a set of benchmarks, not simply as a blind gatekeeper.

In our next article, we examine two factors that could derail the progress made by CAT models in the insurance industry. Model uncertainty and poor data quality threaten to raise skepticism about the accuracy of the models, and that skepticism could inhibit further progress in model development.

Riding Out the Storm: the New Models

In our last article, When Nature Calls, we looked back at an insurance industry reeling from several consecutive natural catastrophes that generated combined insured losses exceeding $30 billion. Those massive losses were a direct result of an industry overconfident in its ability to gauge the frequency and severity of catastrophic events. Insurers were using only history and their limited experience as their guide, resulting in a tragic loss of years’ worth of policyholder surplus.

The turmoil of this period cannot be overstated. Many insurers went insolvent, and those that survived needed substantial capital infusions to continue functioning. Property owners in many states were left with no affordable options for adequate coverage and, in many cases, were forced to go without any coverage at all. The property markets seized up. Without the ability to properly estimate how catastrophic events would affect insured properties, it looked as though the market would remain broken indefinitely.

Luckily, in the mid 1980s, two people on different sides of the country were already working on solutions to this daunting problem. Both had asked themselves: If the problem is lack of data because of the rarity of recorded historical catastrophic events, then could we plug the historical data available now, along with mechanisms for how catastrophic events behave, into a computer and then extrapolate the full picture of the historical data needed? Could we then take that data and create a catalog of millions of simulated events occurring over thousands of years and use it to tell us where and how often we can expect events to occur, as well as how severe they could be? The answer was unequivocally yes, but with caveats.

In 1987, Karen Clark, a former insurance executive out of Boston, formed Applied Insurance Research (now AIR Worldwide). She spent much of the 1980s with a team of researchers and programmers designing a system that could estimate where hurricanes would strike the coastal U.S., how often they would strike and ultimately, based on input insurance policy terms and conditions, how much loss an insurer could expect from those events. Simultaneously, on the West Coast at Stanford University, Hemant Shah was completing his graduate degree in engineering and attempting to answer those same questions, only he was focusing on the effects of earthquakes occurring around Los Angeles and San Francisco.

In 1988, Clark released the first commercially available catastrophe model for U.S. hurricanes. Shah released his earthquake model a year later through his company, Risk Management Solutions (RMS). Their models were incredibly slow, limited and, according to many insurers, unnecessary. However, for the first time, loss estimates were being calculated based on actual scientific data of the day along with extrapolated probability and statistics in place of the extremely limited historical data previously used. These new “modeled” loss estimates were not in line with what insurers were used to seeing and certainly could not be justified based on historical record.

Clark’s model generated hurricane storm losses in the tens of billions of dollars while, up until that point, the largest insured loss ever recorded did not even reach $1 billion! Insurers scoffed at the comparison. But all of that quickly changed in August 1992, when Hurricane Andrew struck southern Florida.

Using her hurricane model, Clark estimated that insured losses from Andrew might exceed $13 billion. Even in the face of heavy industry doubt, Clark published her prediction. She was immediately derided and questioned by her peers, the press and virtually everyone around. They said her estimates were unprecedented and far too high. In the end, though, when it turned out that actual losses, as recorded by Property Claims Services, exceeded $15 billion, a virtual catastrophe model feeding frenzy began. Insurers quickly changed their tune and began asking AIR and RMS for model demonstrations. The property insurance market would never be the same.

So what exactly are these revolutionary models, which are now affectionately referred to as “cat models?”

Regardless of the model vendor, every cat model uses the same three components:

  1. Event Catalog – A catalog of hypothetical stochastic (randomized) events, which informs the modeler about the frequency and severity of catastrophic events. The events contained in the catalog are based on millions of years of computerized simulations using recorded historical data, scientific estimation and the physics of how these types of events are formed and behave. Additionally, for each of these events, associated hazard and local intensity data is available, which answers the questions: Where? How big? And how often?
  2. Damage Estimation – The models employ damage functions, which describe the mathematical interaction between building structure and event intensity, including both their structural and nonstructural components, as well as their contents and the local intensity to which they are exposed. The damage functions have been developed by experts in wind and structural engineering and are based on published engineering research and engineering analyses. They have also been validated based on results of extensive damage surveys undertaken in the aftermath of catastrophic events and on billions of dollars of actual industry claims data.
  3. Financial Loss – The financial module calculates the final losses after applying all limits and deductibles on a damaged structure. These losses can be linked back to events with specific probabilities of occurrence. Now an insurer not only knows what it is exposed to, but also what its worst-case scenarios are and how frequently those may occur.

Screenshot-2014-11-13-14.50.41

When cat models first became commercially available, industry adoption was slow. It took Hurricane Andrew in 1992 followed by the Northridge earthquake in 1994 to literally and figuratively shake the industry out of its overconfidence. Reinsurers and large insurers were the first to use the models, mostly due to their vast exposure to loss and their ability to afford the high license fees. Over time, however, much of the industry followed suit. Insurers that were unable to afford the models (or who were skeptical of them) could get access to all the available major models via reinsurance brokers that, at that time, also began rolling out suites of analytic solutions around catastrophe model results.

Today, the models are ubiquitous in the industry. Rating agencies require model output based on prescribed model parameters in their supplementary rating questionnaires to understand whether or not insurers can economically withstand certain levels of catastrophic loss. Reinsurers expect insurers to provide modeled loss output on their submissions when applying for reinsurance. The state of Florida has even set up a commission, the Florida Commission on Loss Prevention Methodology, which consists of “an independent body of experts created by the Florida Legislature in 1995 for the purpose of developing standards and reviewing hurricane loss models used in the development of residential property insurance rates and the calculation of probable maximum loss levels.”

Models are available for tropical cyclones, extra tropical cyclones, earthquakes, tornados, hail, coastal and inland flooding, tsunamis and even for pandemics and certain types of terrorist attacks. The first set of models started out as simulated catastrophes for U.S.-based perils, but now models exist globally for countries in Europe, Australia, Japan, China and South America.

In an effort to get ahead of the potential impact of climate change, all leading model vendors even provide U.S. hurricane event catalogs, which simulate potential catastrophic scenarios under the assumption that the Atlantic Ocean sea-surface temperatures will be warmer on average. And with advancing technologies, open-source platforms are being developed, which will help scores of researchers working globally on catastrophes to become entrepreneurs by allowing “plug and play” use of their models. This is the virtual equivalent of a cat modeling app store.

Catastrophe models have provided the insurance industry with an innovative solution to a major problem. Ironically, the solution itself is now an industry in its own right, as estimated revenues from model licenses now annually exceed $500 million (based on conversations with industry experts).

But how have the models performed over time? Have they made a difference in the industry’s ability to help manage catastrophic loss? Those are not easy questions to answer, but we believe they have. All the chaos from Hurricane Andrew and the Northridge earthquake taught the industry some invaluable lessons. After the horrific 2004 and 2005 hurricane seasons, which ravaged Florida with four major hurricanes in a single year, followed by a year that saw two major hurricanes striking the Gulf Coast – one of them being Hurricane Katrina, the single most costly natural disaster in history – there were no ensuing major insurance company insolvencies. This was a profound success.

The industry withstood a two-year period of major catastrophic losses. Clearly, something had changed. Cat models played a significant role in this transformation. The hurricane losses from 2004 and 2005 were large and painful, but did not come as a surprise. Using model results, the industry now had a framework to place those losses in proper context. In fact, each model vendor has many simulated hurricane events in their catalogs, which resemble Hurricane Katrina. Insurers knew, from the models, that Katrina could happen and were therefore prepared for that possible, albeit unlikely, outcome.

However, with the universal use of cat models in property insurance comes other issues. Are we misusing these tools? Are we becoming overly dependent on them? Are models being treated as a panacea to vexing business and scientific questions instead of as the simple framework for understanding potential loss?

Next in this series, we will illustrate how modeling results are being used in the industry and how overconfidence in the models could, once again, lead to crisis.

When Nature Calls: the Need for New Models

The Earth is a living, breathing planet, rife with hazards that often hit without warning. Tropical cyclones, extra-tropical cyclones, earthquakes, tsunamis, tornados and ice storms: Severe elements are part of the planet’s progression. Fortunately, the vast majority of these events are not what we would categorize as “catastrophic.” However, when nature does call, these events can be incredibly destructive.

To help put things into perspective: Nearly 70% (and growing) of the entire world’s population currently lives within 100 miles of a coastline. When a tropical cyclone makes landfall, it’s likely to affect millions of people at one time and cause billions of dollars of damage. Though the physical impact of windstorms or earthquakes is regional, the risk associated with those types of events, including the economic aftermath, is not. Often, the economic repercussions are felt globally, both in the public and private sectors. We need only look back to Hurricane Katrina, Super Storm Sandy and the recent tsunamis in Japan and Indonesia to see what toll a single catastrophe can have on populations, economies and politics.

However, because actual catastrophes are so rare, property insurers are left incredibly under-informed when attempting to underwrite coverage and are vulnerable to catastrophic loss.

Currently, insurers’ standard actuarial practices are unhelpful and often dangerous because, with so little historical data, the likelihood of underpricing dramatically increases. If underwriting teams do not have the tools to know where large events will occur, how often they will occur or how severe they will be when they do occur, then risk management teams must blindly cap their exposure. Insurers lacking the proper tools can’t possibly fully understand the implications of thousands of claims from a single event. Risk management must place arbitrary capacity limits on geographic exposures, resulting in unavoidable misallocation of capital.

However, insurers’ perceived success from these arbitrary risk management practices, combined with a fortunate pause in catastrophes lasting multiple decades created a perfect storm of profit, which lulled insurers into a false sense of security. It allowed them to grow to a point where they felt invulnerable to any large event that may come their way. They had been “successful” for decades. They’re obviously doing something right, they thought. What could possibly go wrong?

Fast forward to late August 1992. The first of two pivotal events that forced a change in the attitude of insurers toward catastrophes was brewing in the Atlantic. Hurricane Andrew, a Category 5 event, with top wind speeds of 175 mph, would slam into southern Florida and cause, by far, the largest loss to date in the insurance industry’s history, totaling $15 billion in insured losses. As a result, 11 consistently stable insurers became insolvent. Those still standing either quickly left the state or started drastically reducing their exposures.

The second influential event was the 1994 earthquake in Northridge, CA. That event occurred on a fault system that was previously unknown, and, even though it measured only a 6.7 magnitude, it generated incredibly powerful ground motion, collapsing highways and leveling buildings. Northridge, like Andrew, also created approximately $15 billion in insured losses and caused insurers that feared additional losses to flee the California market altogether.

Andrew and Northridge were game changers. Across the country, insurers’ capacity became severely reduced for both wind and earthquake perils as a result of those events. Where capacity was in particularly short supply, substantial rate increases were sought. Insurers rethought their strategies and, in all aspects, looked to reduce their catastrophic exposure. In both California and Florida, quasi-state entities were formed to replace the capacity from which the private market was withdrawing. To this day, Citizens Property Insurance in Florida and the California Earthquake Authority, so-called insurers of last resort, both control substantial market shares in their respective states. For many property owners exposed to severe winds or earthquakes, obtaining adequate coverage simply isn’t within financial reach, even 20 years removed from those two seminal events.

How was it possible that insurers could be so exposed? Didn’t they see the obvious possibility that southern Florida could have a large hurricane or that the Los Angeles area was prone to earthquakes?

What seems so obvious now was not so obvious then, because of a lack of data and understanding of the risks. Insurers were writing coverage for wind and earthquake hazards before they even understood the physics of those types of events. In hindsight, we recognize that the strategy was as imprudent as picking numbers from a hat.

What insurers need is data, data about the likelihood of where catastrophic events will occur, how often they will likely occur and what the impact will be when they do occur. The industry at that time simply didn’t have the ability to leverage data or experience that was so desperately needed to reasonably quantify their exposures and help them manage catastrophic risk.

Ironically, well before Andrew and Northridge, right under property insurers’ noses, two innovative people on opposite sides of the U.S. had come to the same conclusion and had already begun answering the following questions:

  • Could we use computers to simulate millions of scientifically plausible catastrophic events against a portfolio of properties?
  • Would the output of that kind of simulation be adequate for property insurers to manage their businesses more accurately?
  • Could this data be incorporated into all their key insurance operations – underwriting, claims, marketing, finance and actuarial – to make better decisions?

What emerged from that series of questions would come to revolutionize the insurance industry.