Chairman Davis, Vice Chair Dumais and members of the House Economic Affairs Committee, my name is Alan Smith, and I am a senior fellow and Midwest Director at the R Street Institute, which is a nonprofit, nonpartisan, public policy research organization.
Our mission is to engage in policy research and outreach to promote free markets and limited, effective government in many areas. In this case, we want to protect underwriting tools that promote fairness in insurance pricing and access to the broader market, which is why HB 168 and HB 221 are of particular interest to us. We have engaged on insurance regulatory issues since our founding nine years ago and part of my testimony was previously submitted by my colleague Ray Lehmann in the last session regarding Senate Bill 17.
I write in opposition to HB 168 and 221, legislation that would completely bar the use of credit history in rating risks underwritten by private-passenger auto-insurance policies. How credit is used in property and casualty insurance is not widely understood, so bills that prohibit insurers from using credit in underwriting and rate-setting, place insureds in operating companies with preferred rates or award discounts for good credit have been considered in most states over the past 20 years. Restricting use of credit factors can seem like a reasonable public policy if one does not have to consider alternatives to how rating engines can be built without being unfairly discriminatory.
Before any state government enacts any new prohibitions, it might be useful to ask the carriers what they will do instead to predict the eventual losses, and whether that might be preferred by most of their customers. It is important to remember that part of what attracted the companies to use the formulas that included elements of credit reporting is that they were, in some sense, the fairest systems devised to date. The formulas that were developed did not depend on income, ethnicity, residence, cultural background, gender, age, sex or any of the factors people resent because they are considered “unfairly discriminatory.”
Auto and property insurance prices are always just sophisticated guesses because the actual cost of the product is never known when the price is calculated. Companies that sell auto insurance products spend years working on their rating engines, because guessing low could bankrupt them and guessing high would push sales out to their competition. So, they work diligently to match risk profiles with the prices they charge.
Over the past 30 years, the use of generalized linear models (GLM) to create credit-based insurance scores has revolutionized the personal auto line of business. The discovery of actuarially credible variables tied to credit information has allowed insurers to construct tremendously innovative models that can assign a proper rate to virtually any potential insured. What sold insurers on using these black box scoring formulas was that they produced good loss predictability, particularly when married up with driving records in states that kept good records. With these formulas, the odds that a relationship between the credit score and relative loss ratios does not exist for a given random sample of policyholders are usually in the range of 500-to-1, 1,000-to-1 or even 10,000-to-1, according to industry information provided to regulators. 
A common misconception is that credit-scoring by insurers is analogous to credit-reporting used in banking, where the product is an assessment of capacity to pay back a loan. Instead, by using up to 50 different elements of how people manage their financial affairs, insurers and the vendors who craft these models figured out that aggressive use of credit often indicates aggressive use or even overutilization of an insurance policy, compared to other customers. Extensive use of no interest for first-year purchases and dozens of other indications point irrefutably to customers who are more likely to want a new carpet because of dripped candle wax or who refuse to accept remanufactured wheels for their Porsche which they raced into a ditch. “More likely” is the key phrase here, but if it is 1,000 times more likely, the insurer has enough predictability to figure out charges with some confidence.
R Street believes this legislation would have a deleterious effect on the competitiveness of Maryland’s already-concentrated private auto insurance market, lead to more policies being transferred to the high-risk Maryland Auto Insurance Fund (MAIF), and leave consumers with fewer attractive and affordable auto insurance products.
To the extent that it reverses this progress toward vibrant competitive markets, moving to outlaw the use of credit in insurance rate-setting would cause significant disruption in any state. It should be of particular concern in Maryland, which already struggles with a less than vibrant auto-insurance market. Maryland received a grade of “C-” and ranked 39th out of 50 states in the R Street Institute’s 2020 Insurance Regulation Report Card, as its auto-insurance market scored particularly poorly on benchmarks of competitiveness.  With a market share of 1.57 percent of personal auto premiums, MAIF remains the fourth-largest residual auto insurance market in the nation and one of only five states with shares over 1 percent of the market. 
Banning the use of credit in rate-setting would do further damage to the competitiveness of Maryland’s auto-insurance market. It also would be reflected immediately in a lower score in the R Street report card, which separately assigns demerits for statutory prohibitions that restrict underwriting freedom. Maryland’s score already reflects that it is currently one of just two states, along with California, to bar the use of credit in homeowners insurance rating.
Studies by, among others, the Texas Department of Insurance and the Federal Trade Commission demonstrate conclusively that credit factors are predictive of future claims.  What’s more, the Maryland Insurance Administration already has at its disposal extensive regulatory tools to ensure that rates submitted by auto insurers are not excessive, insufficient or unfairly discriminatory. We urge the committee to reject this legislation.
  See Testimony of the Insurance Institute of Michigan, June 16, 2011. http://legislature.mi.gov/documents/2011-2012/CommitteeDocuments/ 
  R.J. Lehmann, “2020 Insurance Regulation Report Card,”R Street Policy Study, No. 216 (December 2020). https://www.rstreet.org/wp-content/uploads/2020/12/Final-Insurance-Report-card-2020.pdf 
  Ibid.
 Federal Trade Commission, Credit-Based Insurance Scores: Impacts on Consumers of Automobile Insurance, U.S. Department of Commerce, July 2007. https://www.ftc.gov/sites/default/files/documents/reports/credit-based-insurance-scores-impacts-consumers-automobile-insurance-report-congress-federal-trade/p044804facta_report_credit-based_insurance_scores.pdf. 
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- “http://legislature.mi.gov/documents/2011-2012/CommitteeDocuments/”: http://legislature.mi.gov/documents/2011-2012/CommitteeDocuments/House/Insurance/Testimony/Committee12-6-16-2011.pdf
- “House/Insurance/Testimony/Committee12-6-16-2011.pdf”: http://legislature.mi.gov/documents/2011-2012/CommitteeDocuments/House/Insurance/Testimony/Committee12-6-16-2011.pdf
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- “https://www.rstreet.org/wp-content/uploads/2020/12/Final-Insurance-Report-card-2020.pdf”: https://www.rstreet.org/wp-content/uploads/2020/12/Final-Insurance-Report-card-2020.pdf%20
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- “https://www.ftc.gov/sites/default/files/documents/reports/credit-based-insurance-scores-impacts-consumers-automobile-insurance-report-congress-federal-trade/p044804facta_report_credit-based_insurance_scores.pdf.”: https://www.ftc.gov/sites/default/files/documents/reports/credit-based-insurance-scores-impacts-consumers-automobile-insurance-report-congress-federal-trade/p044804facta_report_credit-based_insurance_scores.pdf