Actuaries and underwriters are always looking for a way to evaluate the actual loss potential of the risks the insurance company is writing.  This evolution in risk evaluation is a basic component of the competitive insurance marketplace.  If I can develop a way to more accurately segment risks, then I can have a competitive advantage over those who cannot see the marketplace as clearly.  Data mining is being used to find the next rating variable that has not yet been explored.  This increasingly complex underwriting is costly to develop and sometimes to collect.  Several new developments in personal lines rating have shifted some of the economic burden of providing data to the insured. 

Two such examples are the advent of pay-as-you-drive programs in personal auto programs and wind loss mitigation programs in property programs.  In both the insured is asked to shoulder the economic burden of providing information for the promise of potential large savings in insurance premiums.  In pay-as-you-drive programs, the insured is required to install a monitoring device in their car that transmits information to the insurance company on driving behavior.  Wind loss mitigation programs are focused on recognizing the decreased loss potential associated with certain building features that increase resistance to wind damage (shutters, roof to wall connections, opening protection, etc.).   The economic burden in pay-as-you-drive programs is the loss of personal privacy required of the insured.  The cost in wind loss mitigation programs is more direct; the building inspections required to verify the building features can be quite costly.

These costs are important as they trigger another buying decision point in the underwriting process.  “What type of car do you drive?” “How old is your home” “How many stories?” These questions are expected in the insurance purchasing process and are virtually free to provide and collect.  When the insured is asked to take on a large economic cost in order to provide information, then an additional (and sometimes unexpected) buying decision occurs.  Because of this additional buying decision, the process of collecting the additional information can be subject to selection bias.  What is selection bias you might ask? Two examples might make this term easy to explain.

Let’s say that I approached a group of graduating college students.  Assume this group comprises a mixture of the population of graduating seniors (C-averages students, Art majors, engineers, etc.).  If I propose that I will pay any member of the group $100 if they can answer one difficult math question (or art question, or engineering question), all members of the group would be expected to participate as the reward is far greater than the cost (a minute of time).  At the end of the experiment, I would pay those who could answer the question correctly, but I would also have a very good idea of those of the group who were math majors (or art majors, or engineering majors).  I clearly lost money in this proposition, but gained information.

On the other hand, let’s assume that I approached the same group of students and made a different proposal.  If I propose that I will pay any member of the group $400 if they can answer the one difficult math question, but entry into the contest will cost $150.  In this test, I will probably not get the entire group to participate, as individuals with no math background would choose not to pay the $150 for what they view as a limited chance of getting $400.  However, I may well get the same number of math majors to participate (although we aren’t the most risk loving of souls). 

The results of the two tests may wind up showing me the same individuals as being the math majors.  However, in the second test, the population of individuals participating will be overrepresented by these individuals.  If one were to assume the results of the second test were an unbiased sample, you would draw the incorrect conclusion about the percentage of the overall population that was math majors. 

This same reasoning applies to these costly data items.  Individuals who understand that they are high usage (or just bad) drivers will not allow more specific information about their driving habits be sent to the insurance company.  These individuals do not have a reasonable expectation that the cost they will bear will be rewarded with any great probability.  Similarly, homeowners will likely not pay to have an inspection performed unless they are reasonably sure that they can expect substantial insurance savings. 

The response to this selection bias problem in these two instances is a study of contrasts.  I recently saw one major auto carrier install a premium credit for their pay-as-you-drive program.  All insured who agreed to the program received a discount.  As a result, the economic cost to the policyholders was reduced, if not shifted entirely to the insurance company.  As a result, this company can expect to get an unbiased sampling of the driving habits of their insured population and groups of high-risk and low-risk drivers will naturally segregate through time.

On the other hand, the Florida property market (where a large majority of the wind loss mitigation rating issues have arisen) is already a distressed market.  Companies have been unwilling to bear the cost of the inspection process internally, as most of the market is still struggling with other major cost issues.  Other public programs have not provided large subsidies to insureds for these inspection costs.  As a result, the economic costs continue to be borne by the insured, and a selection bias can be expected to occur.  The population that is inspected is expected to represent a larger portion of the heavily mitigated properties.  The other risks would therefore represent a larger portion of non-mitigated properties.  The pricing of the non-inspected properties would not naturally move to their expected cost.  Pricing deficiencies would have to first show themselves and direct pricing action would have to be taken to adjust these risks to their appropriate rates. 

An additional concern for this product is that a large majority of the pricing is done using catastrophe modeling.  These models rely only on the data they are presented in order to estimate loss potential.  In the situation above, the actual mitigation features on inspected properties would be used in modeling where available.  However, where information is unknown, it is customary for the models to assume that risk potential is based on the average building stock characteristics of an area and time of construction (predominate building code requirements, etc.).  If the model estimates were generated in this manner, the non-inspected populations estimated loss potential would be biased low.  This problem would only be resolved through direct action or through re-calibration of the models to this effect.

These data collection issues are not isolated to the insurance industry, but are present in many other disciplines.  Similar concerns arise in other social sciences and in medial trials.  As our world becomes increasingly data driven, these biases and systematic data collection items present additional hurdles to understanding issues more clearly. 

What are your thoughts on this phenomenon?  What steps could be taken to measure this bias and what potential corrections could be implemented?  What other data collection issues concern you or your organization?

Ryan Purdy, FCAS, MAAA, is a consulting actuary at Merlinos & Associates.

The Insurance Institute for Business & Home Safety (IBHS) is an organization made up of insurers and reinsurers conducting business in the United States.  Its mission is to perform research and promote actions that protect homes and businesses from loss resulting from natural disasters.

In recent years, the insurance industry and the general public have experienced significant losses from hurricanes and tornados.  According to the Insurance Information Institute, of the 14 most costly U.S. disasters, 12 are hurricanes or tornadoes:

IBHS has developed programs for new (FORTIFIED for Safer Living) and existing (FORTIFIED for Existing Homes) homes which specify standards for building and retrofitting homes to better withstand these natural disasters:

• FORTIFIED for Safer Living is a package of code-plus construction requirements that strengthen a home’s roof and wall systems, openings (e.g., windows and doors), and foundation.  Currently about 200 homes meet the Fortified for Safer Living requirements.
• FORTIFIED for Existing Homes was launched in 2010, and provides standards for strengthening existing homes through retrofit techniques at the bronze, silver and gold levels:

The bronze level addresses improving the roof system and attic ventilation system.

The silver level addresses improving exterior opening protection, in addition to meeting bronze requirements.

The gold level addresses, in addition to meeting bronze and silver requirements, the design and installation of a continuous load path, which is a method of construction similar to a chain that ties the house together from the roof to the foundation

FORTIFIED construction has been tested in real life.  Prior to Hurricane Ike, IBHS designated 17 FORTIFIED for Safer Living homes in Galveston, TX.  Of these 17 homes, 14 survived Hurricane Ike.  The three homes that did not survive were damaged by neighboring houses that did not meet FORTIFIED requirements.  These neighboring homes were washed off their foundations and slammed in to the FORTIFIED homes.

BEFORE HURRICANE IKE:

AFTER HURRICANE IKE:

In addition to these real-life tests, which we hope are few and far between, IBHS has a Research Center, which was inaugurated in 2010.  The Center is designed to allow researchers to test various construction materials and systems for the purpose of building homes that can better resist nature’s perils.  In one test at the Center, two homes, one FORTIFIED and one not, are subjected to simulated category 3 hurricane winds.  The FORTIFIED home survived with minor damage while the other home was destroyed.

Of course, the protection of FORTIFIED construction comes at an additional cost.  It is currently estimated that FORTIFIED construction adds 5% to 10% to building costs.  In an effort to demonstrate that FORTIFIED construction is within reach of the average home buyer, Habitat for Humanity has built a home in Alabama which received the FORTIFIED designation.  It was estimated that meeting the FORTIFIED requirements cost Habitat a total of $1,000 to $1,500 more than a home built to the standard code.  In return, however, homeowners receive discounts on insurance premiums, and most of all, peace of mind.   Is FORTIFIED construction worth the additional cost?  What do you think?

Spanning the globe from Japan to New Zealand to Thailand to Iceland to the United States, a series of natural disasters in the past two years caused significant supply chain disruptions.  These disasters have shown that supply chain exposures are an ongoing risk for many businesses, and that this risk can cause serious financial and reputational consequences.  Along with earthquakes and adverse weather activity, other major causes of supply chain disruptions are unplanned outage of IT or telecommunication systems, transport network disruption, insolvency, civil unrest/conflict, and cyber attack. 

According to a survey of corporate risk managers and financial executives conducted by Dempsey Partners in February 2012, 61% said they had experienced a supply chain disruption in the last five years that led to a loss of earnings.  Companies are increasingly relying on their supply chains to produce their products and deliver to their customers.  With the rise of online communication and worldwide delivery, both large and small businesses are subject to supply chain risks.  The exposure does not end at a company’s direct supplier but extends to suppliers of their suppliers as well. 

In addition to not receiving the product for which the company has contracted, the disruption can come from discovering that a product is not to the anticipated standards, which have resulted in massive recalls in recent cases.

So how does your company address the supply chain exposure?  Is the exposure addressed in your commercial insurance policies or in an alternative risk transfer mechanism?  Have you experienced any supply chain disruption in the past 5 years?

It’s no secret that claim costs for Personal Injury Protection (PIP) coverage in Florida are high and due in large part to insurance fraud.  In an earlier blog we asked if Florida should repeal the PIP requirements for motorists.  The Florida lawmakers have answered that question, and their answer is…NO.  Instead, they want to fix it.

The Florida legislature has passed a bill, HB 119, which is expected to be signed by the Governor, that revises the Florida Motor Vehicle No-Fault Law by amending the laws governing PIP benefits and the laws related to PIP motor-vehicle insurance fraud.  A major changes addresses PIP medical benefits such as injured persons must seek initial treatment within 14 days of the accident.  Also, the full $10,000 medical benefit is available only if it is determined that the injured person had an emergency medical condition; otherwise, the medical benefit is limited to $2,500.  The bill also addresses areas related to the PIP death benefit, medical fee schedule, attorney fee awards, investigation and claim payments by insurers, and numerous provisions designed to curtail PIP fraud.  A summary of the major changes in the bill can be found at the Florida Senate Web site.

The bill also states that the Office of Insurance Regulation must contract with a consulting firm to calculate the expected savings and present their findings to the Governor and Legislature by September 15, 2012.  The bill further mandates insurers to file rate decreases over the next two years unless it can explain in detail why it can’t achieve the savings presumed in the mandatory rate changes.  In particular, each insurer must submit a PIP rate filing by October 1, 2012 that decreases its current rates by at least 10%.  A second rate filing must be made by January 1, 2014 whereby the proposed PIP rates are at least 25% lower than the rates that were in effect on July 1, 2012.  In a recent article, the Miami Herald summarizes some of the key changes and brings up some new legal issues that may arise from these reforms. 

Tell us your thoughts: Do you think the new PIP laws will have the anticipated impact on insurance fraud or just create a more sophisticated criminal?  Do you think the changes to the PIP medical benefits will lower the cost of claims and how will the 14 day period to seek initial treatment impact these costs?  And, do you think it’s too soon to be requiring insurers to lower rates before the impact of the changes are studied?   Let us know what you think.

Financial statements from 2006 through 2010 for property and casualty insurers seem to indicate positive news: in the aggregate, the industry has demonstrated favorable development in each of those years.  “Favorable development” refers to a situation such as the following, involving the fictional XYZ Insurance Company, which began writing business in 1995: 

At 12/31/2009, the total incurred loss and defense and cost containment expense (“DCCE”) for exposures in the 1995 – 2009 years for XYZ was carried at $50 million.  “Incurred” loss is determined by adding total payments to carried reserves (including both case reserves and IBNR).  By 12/31/2010, updated payment information and revised reserve estimates place carried incurred loss and DCCE for the 1995-2009 exposure years at $49 million.  We would say that XYZ has exhibited $1 million of favorable development at 12/31/2010.  This development can fluctuate upwards and downwards, and is dependent on total payments and reserve estimates at a given point in time.

A recent article in Contingencies magazine illustrates that favorable development can be illusory.  For instance, the 1995 exposure year exhibited industry-wide favorable development in 1996, 1997, 1998, 1999, and 2000, but then exhibited adverse development in the subsequent five years.  (“Adverse development” refers to a situation in which the estimate of total incurred loss and DCCE has increased over time).  The adverse development exceeded the favorable development by $5.3 billion.

The author cites a number of explanations for the adverse development in the 1995 exposure year, including asbestos and environmental claims and workers’ compensation claims.  Similar trends of initial favorable development followed by adverse development are exhibited in the 1996 through 1998 exposure years.  The author also compares the favorable development exhibited in recent exposure years – 2005 through 2009 – to that in the 1995 to 1998 exposure years and, at least on the surface, the patterns look similar.

Is this recent favorable development real or illusory?  What are the big surprises awaiting the P&C insurance industry related to recent exposure years?

After a devastating tornado season that ravaged much of the Southern United States in 2011, we may not be in the clear yet.  According to AccuWeather.com, we should be gearing up for what could be another record-breaking tornado season in the United States.

Warmer-than-normal temperatures in the Gulf of Mexico are expected to lead to another year of severe storm activity.  Following a year that produced over $25 billion in insured losses related to thunderstorm-tornado-hail activity, insurance companies may want to start battening down the hatches to prepare for what could be another year full of gloomy results.

Not only should insurers be thinking about what the 2012 forecast could mean, but insureds should also consider getting more familiar with their homeowners and auto policies.  It isn’t uncommon for policyholders to identify weaknesses in their coverage after a natural disaster strikes.  Most people likely don’t know whether they have coverage for damage until after attempting to file a claim.  Almost as upsetting as the disaster itself are the stories that pile up about homeowners that are left without a roof over their head because they didn’t have insurance to cover their loss. 

But maybe this year we will be more proactive.  Maybe this year the average consumer will reflect upon the devastation of 2011 and educate themselves about their own insurance coverage.  Or maybe we will just fall back on the norm – it would never, and could never happen to me! 

What are your disaster recovery plans for 2012?  Have you done your homework and know what is and is not covered on your own insurance policies?  What impact do you think the 2012 tornado season will have on both consumers and insurance providers?

Back in the seventh grade, the teacher hung a poster on the wall with the slogan, “In life, as in chess, forethought always wins.” It seems more insurers are taking this to heart with regard to IT spending. IT spending can be described as a case of “pay me now, or pay me later.” Recent trends indicate that more than half of insurers plan to increase IT spending in 2012 with a focus on reducing expenses and improving customer service. The past practice of implementing “siloed” IT infrastructure projects may be gone as well, as more systematic integrated approaches to IT improvements now seem to be required.

As an example, the last 10 years have seen the advent of business intelligence (BI) projects through data-warehousing. Such projects can go a long way toward reducing long-term expenses and improving customer service, assuming companies can absorb the initial IT costs. Duplicate and overlapping information systems can be replaced and single source data reporting capabilities can drive efficiencies. Carefully planning and expense management are needed when implementing such IT projects, but long-term cost savings and improved data access and data quality can pay rich dividends. Insurers certainly wouldn’t mind additional long-term cost savings, and actuaries wouldn’t mind better quality data.

How about you? Is your insurance company planning to increase IT spending? Do you think there is a benefit to increased IT spending? In general, are insurers spending enough on IT?

According to a recent article in the Atlanta Journal-Constitution, homeowners’ insurance price increases are affecting most consumers.  In Georgia, the three main companies raised rates between 7% and 23.9%. 

Industry experts say the increases are due to two main reasons – catastrophe claims and insurance fraud, primarily roofing scams.  2011 had more federal disaster declarations than any other year in history, which has severely taxed insurance companies’ reserves.  For every $1.00 paid in premium, insurance companies are paying out $1.085 in claims.  Reinsurance prices have also been on the rise, forcing insurance companies to pass on this expense to consumers.

Coupled with the trend of rising insurance prices, is the fact that home prices in Atlanta just reached their lowest point since 1998.  Many people are stuck in homes they cannot afford to sell, and are having trouble purchasing insurance. 

What do you think the solution is?  Should insurance companies be capped at how much they can raise their rates in any given year?  Should they be able to deny coverage renewal to current clients that have never filed a claim?  Should the government step in and subsidize insurance for homeowners that can no longer afford their insurance?

In a report released yesterday, the GAO urged clarification of the Liability Risk Retention Act (LRRA) to reduce varying interpretations of the Act.  Proposed legislation also amends the LRRA to allow risk retention groups (RRGs) to provide commercial property insurance.  The legislation is known as the Risk Retention Modernization Act, HR 2126, and would standardize corporate governance standards, create federal arbitration which would settle disputes with states, and would allow RRGs to provide commercial property insurance.

The study found that RRGs have been profitable and growing, and have helped improve the availability of commercial liability insurance, especially in niche markets.  However, differing interpretations of LRRA have led to varying state regulations and disputes between RRG managers and state regulators.  For example, some states interpret LRRA to allow RRGs to write contractual liability coverage, whereas other states do not allow this coverage to be written by RRGs.  In 2010, more than 80% of RRGs were domiciled in Vermont, South Carolina, the District of Columbia, Nevada, Hawaii, and Arizona, whereas about 95% of their premium is written outside of their state of domicile.  Presumably, RRGs are often domiciled in these states due to financial and regulatory advantages.

What do you think of the Risk Retention Modernization Act?  Do you think allowing RRGs to provide commercial property insurance would benefit the industry?  Do you believe that clarification of LRRA would encourage the growth of RRGs and improve the availability of commercial insurance?

A recent article in Insurance Journal by Amy O’Connor discusses a new wave of insuring reputational risk due to the exposure created by the boom in the social media.  Your first question might be, “What is reputational risk as it pertains to insurance?”  In the article, the author defines reputational risk as “a company’s risk of having its reputation damaged because of certain events or incidents and the fallout that takes place because of these incidents.”  Another article in the same publication by author Seamus Gillen states that pure reputational risk does not exist but that it occurs “when operational risk and reputational risk combine to create a perfect storm.”  Furthermore, the article cites the BP Deepwater Horizon oil leak in the Gulf of Mexico, Toyota’s failing brakes on its cars, and the hacking of Sony’s customer data as illustrations of reputational risk and describes the process as:

• “Something goes wrong inside a company which is serious enough to threaten some significant aspect of its operations, and a material part of the related revenue generation which underpins the business model. Investors lose confidence – initially because they perceive a threat to the company’s potential for earnings growth, then more substantially when they see no quick fix to the company’s difficulties. These insecurities grow – and this is the important part – when other key stakeholders, whose support is needed to reestablish the equilibrium of the business model, also lose confidence, and leave in droves.”

A second question might be: “Is it an insurable risk?”  The answer: Apparently so.  Ms. O’Connor’s article references three recent programs by Aon with Zurich, Willis, and Chartis that address exposures of reputational risk and offer risk management services to help corporations keep their reputations intact.  Further, an article by Charles Boyle gives a general description of the coverage offered by Aon with Zurich is discussed which offers advice on pre-crisis planning in addition to coverage for losses.  In Ms. O’Connor’s article, Robert Yellen, chief underwriting officer for the executive liability division of Chartis in New York, states that the company’s product provides two categories of coverage.  The first covers “reputation attacks” defined as “a public attack upon a company’s reputation” and the other covers “reputation threats” defined as “acts or events that the company believes, if made public, would have a material impact on the company’s reputation and would be seen as a breach of trust by the company’s stakeholders.”   The Willis product is targeted toward hotels and responds to incidents that lead to, or are likely to lead to, hotel business losses from adverse publicity through any medium and provides cover for lost revenue based on a hotel industry metric that measures revenue per available room.

Do you think reputational risk is the next big thing in insurance coverage?  Can/will the coverage be profitable?  How does one determine that the initial rates are adequate?   What pitfalls do you see in offering this coverage and how susceptible is it to fraudulent claims?  Tell us what you think.