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.

If you are going to be in San Antonio, let us know – Sue would like to say hello.

• Insurers’ ability to charge adequate rates
• Availability and cost of reinsurance for assumed business
• Quality and availability of data from Citizens to evaluate policies
• Ability of an insurer to select policies without regard to geographic location
• Loss ratio of the policies to be assumed

If you are going to be in Tampa, let us know — Peter would like to say hello.

M&A is a leader in coastal property and casualty insurance risk management and actuarial services.

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?

Florida Hurricane Catastrophe Fund Participating Insurer Workshop
Orlando
May 15-16

South Carolina Department of Insurance Public Input Session
Myrtle Beach
June 7

Society of Actuaries Medical School for Actuaries
New Orleans
June 12

Society of Actuaries Health Meeting
New Orleans
June 13-15

Florida Office of Insurance Regulation 2012 Industry Conference
Tallahassee
June 14

If you are going to be at any of these gatherings, let us know — we would love to say hello.

If you are going to be in D.C. please let us know — Dave would like to say hello.

Topics to be discussed include: Principal Drivers of Homeowners Rates; Recent South Carolina Non-hurricane Losses; 2012 Outlook for the Wind Pool; Overview of Voluntary Market; Availability and Affordability of Coastal Property Coverage; Preparedness and Preparation for Hurricane and Severe Storm Season.

If you are going to be in Charleston, let us know — Mark would like to say hello.

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?

If you are going to be in Charleston, let us know – Rosmary would like to say hello.