All posts tagged 'RMS'

Super Models Are Looking Better Than Ever - What Does That Mean For Insureds?

August 20, 2014 20:19
by J. Wylie Donald
A recent article in August’s Best’s Review, The Rise of the Super Models, by Kate Smith (not Kate Upton, sorry), caught our eye.  A lot is going on in the world of computer catastrophe modeling.  First, demand by insurers and reinsurers is up and modeling firms are “broadening the scope of risks and regions that they model, with RMS, AIR Worldwide and CoreLogic EQECAT all set to release new models this year.”  Among other things, all of the top 3 modeling firms are releasing U.S. inland flood models.  This blog has been hard on FEMA and the Corps of Engineers, criticizing the backward-looking nature of flood plain mapping.  It looks like the tools to remedy that deficiency will soon be at hand. Second, modeling firms are shifting from open models to open platforms, which “offer more choice by providing access to models created by third-party suppliers.”  According to Ms. Smith, a catalyst for this change is Oasis Loss Modeling Framework, Ltd., an insurance industry-founded and -funded organization.  According to Oasis’s webpage, “Barriers to entry have restricted the ability of the insurance community to exploit large elements of available research in hazards and vulnerability.”  Such barriers include costs and knowledgeable personnel.  The goal then was to create an “open marketplace for models and data leading to much wider access to understandable tools for catastrophe risk assessment.”  Open platforms (think Linux) can have great benefits; nevertheless, some are skeptical of Oasis’s practicality in that it is not available for off-the-shelf use, is optimized for an expensive IBM platform, and runs slowly on other platforms, among other things.   The implications for policyholders of all this modeling are three-fold:  first, rates; second, policyholders’ own business decisions; and third, others' views of those business decisions. As insurers better understand the risks associated with particular locations their rates will be adjusted accordingly. This can be a good thing if insurers determine they have overestimated the risk, or if other insurers jump into that market and drive prices lower. But it will be a bad thing if the risk was underestimated and prices rise, or insurers flee a particular market as has regularly happened in Florida and other states.  Indeed, at least one state has seen high court approval of the use of models to limit insurance offerings in high risk areas. Modeling can also be a boon to business planning. What does the future likely hold for a particular location? Will water supplies hold up?  Is the flood map reliable or is it outdated?  Is the company compounding its exposure by yet another franchise or mall development in a particular region?  There is no reason that modeling expertise need be restricted to insurance and reinsurance companies.  Other businesses can benefit.  However, as pointed out in Super Models, “Models are not a perfect science; there are subjective opinions involved.”  Accordingly, businesses should be cautious. And what if a business does not bring modeling into its business planning? It is likely that if things go awry and the unpleasantness is substantial and can be attributed to an inadequate forecast, an injured party will assert the failure to model the future was negligent.  A case in point is In re PXRE Group, Ltd., Sec. Litig., 600 F. Supp. 2d 510 (S.D.N.Y. 2009), aff’d, 357 Fed. Appx. 393 (2d Cir. 2009), where a reinsurance company found its failure to rely on a particular model was the gravamen of a class action plaintiff’s security fraud suit.  PXRE was a thriving reinsurance company, whose business was conditioned on maintaining an A- rating.  Unfortunately, Hurricanes Katrina, and then Rita, and then Wilma, devastated certain portions of the Gulf Coast to its reinsureds’ detriment.  PXRE stepped in and paid on its reinsurance contracts but the losses kept increasing.  It relied on models to reassure the investment community that it remained financially sound in order to raise money.  The models it relied on, however, turned out to be inaccurate, and ultimately PXRE's rating crumbled and it succumbed to the unprecedented losses.  The class action ensued. Plaintiff claimed, among other things, that PXRE should have relied on a higher estimated loss ($40-60 billion by RMS) rather than valuations of $30-40 billion touted by PXRE’s own models as well as by ISO and Air Worldwide.  The district court opinion gives a lengthy dissertation on the standards to be applied in a securities fraud case on a motion to dismiss and concluded that PXRE was not reckless in its reliance.  More germane to the issue here, is that PXRE was able to defend itself because it had relied on models.  Granted, modeling was part of PXRE’s business and, no doubt, a lack of modeling would have been reckless.  But, is a prudent non-insurance business going to eschew modeling on the theory that no one else in its industry relies on them.  If models are becoming more widely available, as suggested by Super Models, the path of the prudent business is, at the very least, to consider whether modeling has something to offer. 

Climate Change Effects | Flood Insurance | Insurance

The Maryland Court of Appeals Looks at Models and Likes What it Sees - People's Insurance Counsel Division v. Allstate Insurance Co.: Affirmed

January 28, 2012 21:59
by J. Wylie Donald
Notwithstanding that millions tune in to the long-running reality TV show America's Next Top Model, the real modeling action is not in Hollywood.  Instead, it is on computer mainframes churning out annual simulations of 100,000 years or more of catastrophes such as hurricanes, earthquakes and terrorist attacks. Such analysis drew the attention of the Maryland Court of Appeals in its seminal opinion last Wednesday in People's Insurance Counsel Division v. Allstate Insurance Co. (attached), which affirmed the appropriateness of modeling in an insurer's decision to issue, or not, homeowners' insurance policies. The facts in Allstate were relatively simple. In 2006 Allstate determined that it would no longer write homeowners' policies on Maryland properties within one mile of the Atlantic Ocean. It subsequently extended that decision to completely exclude from new policies five Maryland counties, and portions of an additional six counties (all identified by zip code). It relied on a model developed by Applied Insurance Research, Inc. (AIR), which showed that the hurricane losses Allstate would suffer in the identified zip code areas were too high. Dutifully Allstate filed the appropriate papers with the Maryland Insurance Administration. The Administration found nothing exceptional about the application. The People's Insurance Counsel Division (PICD) (a part of the Office of the Attorney General) did, however, and requested a hearing.  It lost before the Commissioner of Insurance, then before the Circuit Court and again before the Court of Special Appeals (see our post).   PICD then appealed to Maryland's highest court and argued before the Court of Appeals that Allstate had failed to meet its burden of showing that its decision was not "arbitrary, capricious or unfairly discriminatory."  See Md. Ins. Code § 27-501(a)(1).   Following from that, PICD further argued that the designation of areas by zip code did not have an objective basis and therefore was arbitrary and unreasonable. See Md. Ins. Code § 19-107(a).  Allstate's proofs consisted primarily of computer modeling evidence, which the Commissioner found sufficient. Much of the opinion is directed to the parsing of Maryland's Insurance Code and its legislative history to determine whether § 27-501 even applied (the Court of Special Appeals had found it did not, and the Court of Appeals reversed that portion of the decision). We leave it to the insurance blogosphere to address that further. What is of interest to this readership is how modeling came into the decision and where modeling stands as a result. In the proceeding Allstate offered a model that simulates hurricanes from genesis to decay and the damages that would be suffered.  Basically, AIR modelers "developed mathematical functions that describe the interaction between buildings and their contents and the local intensity to which they are exposed." PICD at 7.  Allstate established with expert evidence that catastrophe risk is not diversified ("adding additional catastrophe risk does not reduce overall risk because of pooling but actually increases the overall risk") and that historical loss data is incomplete and outdated "making it difficult to estimate losses."  PICD at 7.  Accordingly, "it has become standard practice for insurance companies to use catastrophe models to anticipate the likelihood and severity of potential future catastrophes before they occur." PICD at 5-6. The advantages of modeling are substantial;  (1) It was able to capture the effects on catastrophic loss distribution of changes over time in population patterns, building codes, amounts insured, and construction costs;(2) It provides a complete picture of the probable distribution of losses rather than just estimates of probable maximum losses; (3) Because simulation models can be tested more easily than other approaches, it leads to greater stability in estimating expected annual losses;(4) It provides a means to determine the impact of new scientific information; and(5) It provides a framework for performing sensitivity analyses and “what if” studies. PICD at 6 As the Court noted, "By using computer models, they can get 100,000 years of simulated loss experience, which is good not just for State-wide pricing but also for loss characteristics related to hurricanes down to the ZIP Code level." PICD at 7.  PICD retained an actuary to rebut Allstate's proofs; he testified with respect to "actuarial science." He was hampered, perhaps fatally, when the Commissioner refused to allow him "to express any opinion with respect to the model that formed the basis of Allstate's amended filing." PICD at 11. We were not there but the Court of Appeals paints a picture of a non-committal expert. He offered that the decision to not write new policies was unreasonable "'because there is no showing that it is reasonable.'" And he "declined to choose" the method Allstate should have chosen to reduce its risk.  PICD at 11. In a post-hearing submission PICD argued that "Allstate was required to produce valid statistical data demonstrating the probability of a hurricane sufficiently strong to cause catastrophic damage actually making landfall in Maryland and that it failed to do so."  PICD at 23.  The statistical standard was based on dicta in an earlier Court of Special Appeals decision, Crumlish v Ins. Comm'r, 520 A.2d 738 (1987), which the Commisioner and the Court distinguished.  First, Crumlish's requirement for statistical evidence was not a universal requirement. PICD at 25. More significant was the "catchall" exception added to § 27-501 which established a "standard approved by the Commissioner that is based on factors that adversely affect the losses or expenses of the insurer under its approved rating plan and for which statistical validation is unavailable or is unduly burdensome." PICD at 25. "That is what the Commissioner did in this case."  PICD at 25.  In other words, the Commissioner found Allstate's evidence met its burden of demonstrating that its use of modeling as the basis to stop writing policies in certain areas was reasonably related to its business and economic purposes and was not discriminatory.  The dissent would have adopted the Crumlish dicta and required Allstate to offer statistical evidence concerning the landfall of destructive hurricanes in Maryland. PICD, dissent at 5.  Such an assessment was either to be based on the historical record (an impossibility as no hurricane had ever made landfall in Maryland) or "climate science" (which one would think would include modeling).  PICD, dissent at 9, 10.  According to the dissent, all Allstate provided was a computation of the "relative risk" of a hurricane landfall in Maryland as once in 25,000 years based on the worst 5% of hurricanes that made landfall in North Carolina, Virginia, and Delaware.  Allstate justified its decision based on hypothetical hurricanes, i.e., a model.  PICD, dissent at 7. The Court properly rejected this distinction.  The use of probabilistic catastrophe risk modeling came of age following the destruction caused by Hurricane Andrew in South Florida in 1992. As stated by modeler RMS in its 2008 A Guide to Catastrophe Modeling (p6):  "It became clear that a probabilistic approach to loss analysis was the most appropriate way to manage catastrophe risk. Hurricane Andrew illustrated that the actuarial approach to managing catastrophe risk was insufficient; a more sophisticated modeling approach was needed."  Another modeling firm, EQECat, put it this way:  "The main concern for all users is the uncertainties in the models. Some time ago, the only way to estimate a probable loss was to trust few statistical studies of past losses from some historical events and or on the experience of the underwriter. The uncertainty in these models was quite large as confirmed once a new event [such as Hurricane Andrew] took place. The main problem is that there is not enough historical data, and the standard actuarial techniques of loss estimation are inappropriate for catastrophe losses."  One of the purposes of catastrophe modeling is to assist the user (often an insurer) in avoiding the alliteratively named "risk of ruin."  If all the industry is using a tool that can minimize the risk of run, it would ill-behoove a court to take away that tool.  In Allstate the Maryland Court of Appeals agreed.  Nevertheless, if one is looking for guidance on how modeling will be received in the courts, there is one significant question left unresolved by this decision:  how will competing models be treated?  PCID's expert seems to have been completely out of his league. Whatever his actuarial credentials, if the issue is modeling then a modeling expert is needed. And at the very least the AIR model was subject to challenge. In a review published just this month, Assessing US Hurricane Risk: Do the Models Make Sense?, AIR takes on its competition, RMS, and states:  "with this latest round of updates, we [modelers] find ourselves more divergent in our views of risk than ever." (p5)  As one example of this divergence, "Catastrophe modeling companies have vastly different views on what influence sea surface temperatures (SSTs) in the Atlantic Ocean have on U.S. hurricane landfall risk." (p12).  If AIR is correct, perhaps application of the RMS model would have altered the list of excluded zip codes. More fundamentally, does the uncertainty established by competing models (and that is inherent in modeling) impose an unavoidable and unacceptable arbitrariness in application?  That is for another day.  For the moment, modeling companies and those who use them likely will proceed full speed ahead. Post scriptum - Climate change seems to have been a subject not to be discussed.  As noted by the dissent, if Allstate was worried about the science of climate change, it didn't bring it up.  Nevertheless, the dissent did bring it up and asserted that meteorological change occasioned by climate change could be a legitimate basis for Allstate's decision.  The modeling firms think otherwise. Eqecat's CEO Bill Keogh has stated because of the uncertainty associated with climate change's effect on hurricanes, " it has no role in catastrophe risk modeling." Peoples Insurance Counsel Division v Allstate Insurance Company.pdf (78.07 kb)

Climate Change | Climate Change Effects | Insurance | Regulation

Ceres and a Series of Serious Thoughts About the NAIC Climate Disclosures - Part II

September 16, 2011 05:09
by J. Wylie Donald
We wrote yesterday to introduce Ceres’ report on the disclosure of climate risks by insurers and considered its first Recommendation to Regulators concerning mandatory and public disclosures.  We address today the second recommendation in Climate Risk Disclosures by Insurers:  Evaluating Insurer Responses to the NAIC Climate Disclosure Survey.    Ceres’ second recommendation is to "[c]reate shared resources around the implications of climate trends on enterprise risk management."  Id. at 51.  In other words, more research should be made available concerning investment risks and opportunities, correlated risks, loss modeling, the potential for loss of health and life, and customer resilience (ability to resist extreme events).  Id.  Taking modeling by way of example, Ceres discusses modeling thoroughly in Part 2 and the discussion is thought-provoking.  Several insurers are conducting climate change modeling internally.  For the rest, they rely on third-party vendors, which invokes much criticism from Ceres.  "The majority of insurers that report using catastrophe models describe them in terms that suggest their company does not have a clear understanding of how the models can or cannot be used to anticipate changing risk.  Most of the industry relies on third-party catastrophe risk models that only marginally integrate changing extreme weather."  Id. at 6.  "[I]nsurers relying entirely on third-party models may be severely unequipped to adjust pricing to incorporate emerging climate risks." Id. at 31.  "Insurers' disclosures suggest that the majority of insurers may be setting pricing based on flawed assumptions of how the industry's loss models incorporate changing climate trends."  Id. at 32. Ceres lauds those companies that can do it in-house.  But specialization and economies of scale are fundamental drivers of the market.  Were every insurer to bring modeling inside, undoubtedly there would be some new insights not presently uncovered.  But there would also be insurers who got the models grievously wrong and, in most cases, the resources spent on modeling would be more cost-effectively spent on other items necessary to delivering products or services. To be sure, reliance on EQECAT, AIR Worldwide and RMS as the sources for all climate change modeling has its flaws.  One need only think back a few years to where another triumvirate dispensing financial ratings (allegedly) misled sophisticated investors around the globe.  But in a world of constrained resources, or even an unconstrained one, third-party modelers are necessary and beneficial.  Further, a disadvantage to society from in-house modeling is that the insights developed from proprietary work may remain just that:  proprietary.  Ceres acknowledges "it is ... possible that asymmetrical information can be used by individual companies to secure a competitive edge against their peers."  Id. at 38.  Indeed, "larger insurers more readily recognize the inherent limitations of current catastrophe models in light of changing climate than do their smaller competitors or clients.  These players have a clear competitive advantage in deploying resources to build the latest climate science into their pricing models."  Id. at 37.  Third-party vendors, on the other hand, spread their best products across many insurers, in effect sharing their best research (but only to those willing to pay for it).  We wrote yesterday of the need to recognize that intellectual capital is a business asset and criticizing a goal of making climate change disclosures public available.  We think those comments apply likewise to the sharing of resources. Nevertheless, Ceres does great work in raising the bar for third-party vendors.  By pointing out to insurer-users that they may not be getting what they really need from the modeling firms, we expect the modelers will have to go out and address Ceres’ criticisms.  For example, insurers are exposed if (as Ceres asserts) "few insured perils are modeled by insurers, leaving the possibility for climate-affected perils to be underpriced."  Id. at 35.  More specifically, "recent years have demonstrated that climate change may be driving up aggregated losses from smaller events, including perils such as floods, snowstorms and hailstorms, in ways that erode insurer profitability."  Id. Tomorrow we conclude our review with a look at Ceres’ third recommendation as well as sharing some concerns about research.

Climate Change | Insurance | Regulation

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