Following is the testimony presented by Lawrence S. Powell, PhD before the United States House of Representatives Financial Services Committee Oversight & Investigations Subcommittee on May 21, 2008, by:
Lawrence S. Powell, Ph.D.
Research Fellow - The Independent Institute (http://www.independent.org/), and
Whitbeck-Beyer Chair of Insurance & Financial Services
University of Arkansas-Little Rock
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Introduction:*
(* Much of this testimony is drawn from a study being written for the Independent Institute.)
Insurance companies face an unusual challenge. They must set prices for the products they sell before they know all of the costs. To meet this challenge, they employ complex pricing methods developed by actuaries using applied economic and statistical techniques. It should then come as no surprise that some aspects of actuarial science and insurance pricing are puzzling to people who have not developed substantial expertise in this field.
Insurance scoring, the use of credit information in insurance underwriting and pricing, is an example of a beneficial practice that is sometimes misunderstood. Insurance scoring benefits consumers in several ways, all of which stem from its accuracy as a predictor of insured losses.
The purpose of my testimony is to present comprehensive information about insurance scoring in a non-technical format. In Section 1, I present a brief conceptual summary of insurance pricing and insurance scoring. In Section 2, drawing from existing studies, I present evidence that insurance scores are powerful and accurate predictors of insurance losses. In Section 3, I conclude with discussion of the appropriateness of insurance scoring.
Section 1: Insurance Pricing and Insurance Scoring
An insurance company facilitates risk pooling, reducing the uncertainty of individual pool members. Uncertainty decreases because the ultimate value of the group's losses is more predictable than that of an individual. Swiss mathematician Jacob Bernoulli first proved this phenomenon, known as the law of large numbers, around 1690. Relying on the law of large numbers, a group of pool participants can each pay the average or expected loss of the group, rather than paying for a much less predictable and potentially larger individual loss on one's own.
Risk pooling is most effective when all members of the pool have the same expected loss. Insurance companies rely on risk classification systems to ensure that groups of insureds pay premiums commensurate with their exposures to risk. When insurers pool exposures with unequal expected losses, the low-risk group must subsidize the high-risk group. This creates an incentive for low-risk pool members to purchase less insurance than high-risk pool members, a scenario called adverse selection. Adverse selection can break down the risk pooling mechanism and, in extreme cases, lead to insolvency of the pool.
Insurance companies use information about applicants for insurance to classify them into groups with very similar expected loss. Of course, no risk classification system is perfect. In addition to other restrictions, insurers can only use rating information if it is cost effective; meaning the cost of obtaining the information is less than the difference in expected loss between groups. For example, assume there are only two types of drivers, low-risk and high-risk. The low-risk group has expected loss of $500 and the high-risk group has expected loss of $700. If it costs more than $100 to classify a driver, it will be more cost effective to simply pool them together and charge both groups $600. However, if an insurer can identify low-risk drivers for, say, $20, it benefits the low-risk drivers to charge them $520, and charge the high-risk drivers $720. On the other hand, insurers could be more precise in risk classification if they hired private investigators to follow each driver for six months before offering an insurance policy. Obviously, this would cost more than $100, and raise privacy concerns. To have enough money in the risk pool to cover expected losses, low-risk drivers would have to pay more than $600. In this case, there is no justification for such an unfair classification.
There are many variables insurers use to classify drivers based on expected loss. These include, but are not limited, to geographic location, age, gender, marital status, miles driven, type of vehicle, use of vehicle, driving record and insurance score. An insurance score is a numerical prediction of propensity for loss estimated using certain information from a driver's credit history. The actuarial literature shows it is one of the most accurate and cost effective loss predictors available (EPIC, 2003).
There are several apparent misconceptions about insurance scores. To understand why insurance scores are beneficial to insurance systems, it is important to start with an accurate description that is free of incorrect assumptions. The variables commonly used to estimate insurance scores include measures of performance on credit obligations, credit-seeking behavior, use of credit, length of credit history, and types of credit used (FTC, 2007). They do not include income, wealth, race, ethnicity, or any prohibited factor.
Insurance scores and credit scores are calculated using some of the same information, but they are not equivalent. The important difference is that credit scores use these variables (and others) to estimate the probability of a borrower defaulting on a financial obligation, while insurance scores estimate the probability of having insured losses.
An important fact often overlooked in the debate about insurance scoring is that the only way including insurance scores in an insurance rating model can result in higher premiums is for the sample population with lower scores to have more insured losses. As I describe in more detail in Section 3, any deviation from using the most accurate, cost effective predictors results in unfair outcomes and damage to the insurance mechanism.
One observed barrier to understanding insurance scoring is manifest in the common criticism that there is not an intuitive link between insurance scores and driving ability. While several studies develop potential causal links between insurance scores and driving, I find it more compelling to recognize an alternative relation. The use of insurance scores does not rely on a link between credit information and "driving ability." Rather, it is a link between insurance scores and insured losses.
There are many factors unrelated to driving ability that increase the likelihood of insured losses. For example, someone who always makes debt payments on time to avoid higher interest rates the next time they borrow may also choose not to file a small insurance claim to prevent an increase in insurance premiums in the future. It may also be the case that insurance scores measure hazards other than lack of driving ability.
(The views expressed in this article/commentary are solely those of the author and do not necessarily represent the views of MyNewMarkets.com, the Insurance Journal or Wells Publishing.)
This is the first of a five-part series. The remaining commentaries can be found at www.mynewmarkets.com under the "Articles" tab.
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When millions of people lose their jobs, their homes, their savings and retirement becaue of the investment meltdown, if credit "insurance" scoring is valid, then there should be a corresponding dramatic increase in auto insurance claims experience. If not, then the lack of intuitive link is because there is a casual connection not a causal connection between credit scoring and loss experience.
Thomas E. Nelson, CIC, ALCM, CRM
For Dr. Powell to claim that these variables don't include income, wealth, race, ethnicity, or any prohibited factor is ludicrous. Who is most likely to be laid off work or have their hours reduced? Who is most likely to obtain credit from a small loan company? Who is most likely to exhibit questionalble credit seeking behavior such as applying randomly for credit cards to obtain a free gift or to perhaps 'roll-over' credit to new card with a teaser rate? Who is most likely to use a larger percentage of their modest credit line? Who is most likely to have 'performance' issues on their credit due to layoffs, unexpected medical bills?
The answer of course are the indigent, minorities, immigrants, people lacking education, single or divorced women, single parents and/or low wage earners. The 'Insurance Score' is effectively a surrogate or shadow for those factors that if used explicitly would be unlawful but are nontheless discriminatory.
The inner-city poor person may be more prone to submit a small claim than the upscale suburbanite because even a small loss has real financial consequence. Also, if that person is uneducated on the insurance process, they are unaware that submitting a small claim may actually cost them more in the long run in terms of higher premiums..
Do people with lower Insurance Scores have more accidents? Probably not but more accidents may happen within the zip codes where they reside. Reason is that heavy commuter traffic through their neighborhoods. If you lived in proximity of downtown Los Angeles or Philladelphia you would have thousands of travelling through your neighborhood. Should accidents occuring by commuters within a zip code of a heavily travelled urban area adversely affect the people who reside in that zip code. The answer is no but when you look at the premium rates for people living in Los Angeles or Philadelphia you will find thm to be higher for those residents than their suburban counterparts.
The insurance Industry has in whole or in part funded these studies to establish a link between a person's credit habits and their alleged propensity for filing claims. The industry marches in lock step on this issue and will repeat their same nonsense till the cows come home. The fact is there is no more real correlation between credit score and propensity for claims than between your astrological sign and claim frequency.
Not only lower credit scores but every group indicator of tight budgets (e.g. lower income zip codes and lower paid occupations) must predict more miles per insured car year for the group and therefore more claims per 100 car years for insurers to pay. Why? Because pay-by-the-car, all-you-can-drive premiums cause financially constrained drivers to insure fewer cars and drive each more miles.
This explanation is further documented by my testimony last Fall to the Financial Services Committee, which, with academic papers on the subject, may be found at www.centspermilenow.org.
If credit scoring were not the most efficient means to align rates and risk, other insurance carriers could use different approaches, provide a more precise alignment of risk to rates, and win the entire market, thereby putting these evil credit scorers out of business. But, it has not happend. It has not happended because credit scoring works, is efficient and economic welfare maximizing. Those using credit scoring have the competitive edge by virtue that other approaches do not work as well.
Those calling for the end of credit scoring fail to see that the inefficiencies created in the market (under pricing risky driver premiums), which means that someone else (good drivers) pay for it. This point was made by consumer group see: http://www.theamericanconsumer.org/category/issues/finance_insurance/
The result of under pricing risky driver premiums, besiding raising everyone else's premiums, encourages risky behaviors. It makes it easier for bad drivers to rack up the miles -- leading to more accidents, claims, losses and deaths. The end result of this cross-subsidy makes the average American worse off.
Your comments in paragraphs 3 and 4, do nothing to reassure those who doubt the use of credit scoring. IF the credit score is applied to a 'group' of drivers (in this case those with lower credit scores) how do you make the leap to 'risky' drivers being paid for by 'good' drivers? You have now taken a group-based data set and begun to apply it to the individual, causal factors of drivers with low credit scores.
The good Doctor does not do this. All of the vendors of this information claim that credit scoring allows you to segregate a 'group' that performs less well than those with good credit. Credit scoring can not tell me, as an underwriter, which risk will have losses.....so I surcharge an entire segment for the losses of those with bad driving habits while others in that segment exhibit good loss experience. Ultimately, the good drivers in the low credit score group pay for the drivers with bad experience. Tough luck for them?!
Having a bad day??
This discussion is what America is all about. We disagree...that's all. Hate? Idiots? Fools? And when do you expect one insurer to change direction based on Comments section of Insurance Journal. Now that is certainly foolish thinking.
I don't know your background, but as someone who is not convinced about the most perfect underwriting tool since sliced bread, I just celebrated (?) my 36th year in the industry. I've had two 'tours' of duty as a Regional Underwriting manager with major (Top 10) insurers. As such, I argued on more than one occasion with the brilliant ideas my superiors would employ. Sometimes I won...most times I lost. It will be the same way with Credit Scoring because of the great sales job of Fair Issac, Equifax etc. Adverse selection is feared and if you don't credit score you will be selected against.
All that being said, I don't 'hate', I am not an 'idiot', I may act foolish buy I am not a 'fool' AND I know enough about underwriting to understand that it is built around discrimination. The best underwriters discriminate risk better than other underwriters.....and I don't need a credit score to tell me that. ;o)
Cool you jets and don't take active discussion personally.
"They" turn in more of what kind of claims? Auto physical damage? You've stil got to show me how that makes it a viable rating/underwriting tool for liability coverage.
I'll go along with the increased cost for physical damage but not liability until you show me the data that says, "drivers with low credit scores are poor drivers because they have, on average, 40% more at fault accidents than those with higher ranges of credit scores." Or, "those with lower scores have, on average, 35% more speeding tickets."
Until that is shown, it's not a liability coverage issue nor should it affect a liability-only policy.
I've heard it before, and I'm hearing it again. If credit scoring is so accurate, then why hide the data? Why be so secretative? Let's see the actual data, not what someone's biased interpretation on what they were paid to say.