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?

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?