2015 saw approximately one-third of U.S. states1 join the first state, Maryland, in issuing guidance to property and casualty insurers with respect to price optimization; action by the National Association of Insurance Commissioners (NAIC) in approving a white paper on price optimization; and increasing focus on the use of big data in insurance ratemaking. In the coming year, regulators’ focus on big data may expand beyond price optimization, even as the remaining states promulgate guidance on this issue.

Recent State Price Optimization Bulletins

Most recently, Colorado, Minnesota, Connecticut, Alaska, and Missouri have brought to 18 the number of states issuing bulletins barring the use of what regulators consider to be price optimization. (In addition, Virginia has provided guidance in its Property & Casualty Handbook, while New York has issued a Section 308 inquiry letter.) These states’ bulletins draw on the work of the NAIC’s Casualty Actuarial and Statistical (C) Task Force, and all but two require revised filings.

The Connecticut Division of Insurance issued Bulletin PC-81 on December 4, 2015. The bulletin defines price optimization as “the use of factors not specifically related to an insured’s expected losses and expenses . . . to help determine or to adjust an insured’s premium.” The bulletin reminds insurers of relevant state law, and then outlines particular factors whose use would be inconsistent with a prohibition on unfairly discriminatory rates. These factors include price elasticity of demand, applied on an individual level; propensity to shop for insurance, ask questions, or file complaints; and retention adjustment at an individual level. However, the bulletin clarifies that “the use of sophisticated data analysis to develop finely tuned methodologies with a multiplicity of possible rating cells” will not necessarily violate state law as long as the rating classifications and rating factors are cost-based. The bulletin directs insurers using price optimization to submit revised filings within 60 days of the bulletin and to disclose their use of non-risk-related factors on their SERFF General Information page.

Alaska’s Bulletin B 15-12, issued on December 8, 2015, is similar to the Connecticut bulletin, except that it does not require insurers to take any particular action. Both Connecticut and Alaska appear to have followed the NAIC Task Force White Paper’s model bulletin.

The Minnesota Department of Commerce also refers to the White Paper’s definition of price optimization in its Administrative Bulletin 2015-3, which was issued on November 16, 2015. The Department further defines price optimization for purposes of the bulletin as “any method of taking into account an individual’s or class’s willingness to pay a higher premium relative to other individuals or classes.” Based on that definition, it concludes that any use of price optimization would violate Minnesota law because “[p]rice optimization is not an actuarial estimate based on expected losses, expenses, and the degree of risk.” Any insurer using price optimization must immediately cease the practice and file revised filings and a corrective action plan in 60 days.

The Colorado Division of Insurance considered both price optimization and elasticity of demand as roughly equivalent in its Bulletin B-5.36. The bulletin, issued on October 29, 2015, defines the practice at issue as “consider[ing] the point at which an insured will look for coverage elsewhere due to increases in the premium charged” and “mak[ing] use of data analysis techniques that measure the willingness of individual insureds to pay higher rates for insurance coverage than other insureds with similar underwriting characteristics.” The bulletin outlines state law and makes clear that risk classifications should be grounded on expected losses and expenses. Previously acceptable practices applied on a group basis and the use of data analysis are not prohibited under the bulletin. The bulletin directs insurers using price optimization to submit a new rate filing within 90 days.

Missouri, whose Department of Insurance, Financial Institutions and Professional Regulation is led by current NAIC President John Huff, is the most recent state to issue guidance on price optimization. Bulletin 16-02, issued January 12, 2016, links to the Task Force White Paper but does not follow the Task Force’s model bulletin. The bulletin defines price optimization as “the use of factors to help determine or to adjust the insured’s premium that are not specifically related to the insured’s risk or hazard.” It notes that insurers may violate insurance laws prohibiting unfair discrimination by using price elasticity of demand, propensity to shop for insurance, ask questions, or file complaints, and retention adjustment at an individual level. The bulletin states that insurers “may be asked to identify and explain their use of price optimization in personal lines rate filings,” and “encourage[s]” any insurer that “self-identifies a potential use of unfairly discriminatory rates . . . to visit with the Department as soon as practicable,” but does not require insurers to submit new rate filings.

Patterns in the State Bulletins

Some patterns and trends can be seen in the state insurance bulletins and guidance on price optimization. Although the definitions vary—indeed, the White Paper and many bulletins acknowledge that “there is no universally accepted definition of price optimization”—many of the states have adopted similar definitions. The vast majority of states (16 of 20) define price optimization as using factors unrelated to the risk of loss in ratemaking. Many states (11) also refer to an insured’s willingness to pay a higher premium (or, in two cases, “the highest price that the market will bear”) at some point in describing the practice of price optimization. Some states (7) also refer to the use of data or sophisticated statistical analysis in the definition of the practice; a few of these states, including Pennsylvania, appear to define price optimization as the use of data without regard to whether that data is tied to expected risk or loss.

Of the 18 states that have issued bulletins to date, 12 have required insurers to revise their filings. In addition, many of the bulletins issued after the NAIC Task Force’s model bulletin have clarified what is not prohibited—including use of sophisticated data analysis and certain pricing practices applied on a group basis.

Activity at the NAIC

As reported previously, the Task Force adopted a final version of its long-awaited White Paper at the NAIC Fall Meeting in November 2015. The White Paper was adopted by the Property and Casualty Insurance (C) Committee as well. Although many states issued guidance without waiting for the Task Force to complete its work, the White Paper may provide some consistency in future state pronouncements regarding price optimization. In fact, states that issued bulletins more recently have generally followed the White Paper in defining price optimization and in clarifying that certain practices were acceptable notwithstanding the restrictions on price optimization.

Other Regulators Focus on Big Data

Price optimization is not the only front on which regulators are focused on the potential impacts of big data on insurance underwriting. Consumer advocacy groups have raised concerns about the use of big data before other NAIC committees as well as on the Task Force. For example, at the NAIC Summer National Meeting, the Market Regulation and Consumer Affairs (D) Committee held a hearing on the use of consumer data in settling property and casualty claims at the request of NAIC consumer representatives. That hearing has led to a charge being given to the (D) Committee in 2016 to explore issues relating to the use of big data in claims, marketing, underwriting, and pricing. Also at the Summer National Meeting, the Life Actuarial Task Force (LATF) of the Life Insurance (A) Committee heard a presentation from Mary Bahna-Nolan, Chair of the Joint American Academy of Actuaries and Society of Actuaries Preferred Mortality Oversight Group, during which she noted that accelerated underwriting, often using predictive analytics and complex algorithms, is an emerging trend in life insurance underwriting. These algorithms may include prescription drug data, credit scoring, electronic lab data, and financial underwriting information, among other factors. The LATF agreed that its Experience Rating (A) Subgroup will address the issue of expansion of the Valuation Manual’s VM-51 data collection requirements to take accelerated underwriting into account.

Outside of the NAIC, consumer advocacy groups have also made presentations on big data and price optimization before the Federal Advisory Committee on Insurance, which provides advice and recommendations to the Federal Insurance Office. And although not directly relevant to insurers, the Federal Trade Commission (FTC) recently issued a report on the use of big data. The FTC recognized the potential value of big data for companies, but raised concerns about how data could be used or interpreted to the detriment of consumers, and especially lower-income communities.

Finally, in the United Kingdom, the Financial Conduct Authority (FCA) is seeking to understand how big data affects consumers, recognizing that the use of big data brings both benefits and risks for consumers. In November 2015, the FCA issued “Call for Inputs: Big Data in Retail General Insurance,” which asks for data on how big data affects consumer outcomes and business competition. It also asks how the FCA’s regulatory framework may affect developments in big data. The FCA is asking for comments on the micro-segmentation of risk and the increased granularity of risk segmentation permitted by big data analytics; on the impact on pricing as insurers are able to use big data to better identify consumer characteristics and behaviors that do not directly affect the consumer’s risk profile, but are important for pricing decisions; and on how consumer behavior may change in response to some applications of big data. For instance, consumer concerns on the increased use of big data could diminish their trust in retail general insurance firms, but it could also provide an incentive to reduce risky behavior. The FCA will make all responses available for public inspection unless the respondent requests otherwise.

As the use of big data becomes more widespread—and as consumers and regulators become more aware of its implications—regulators are expected to focus on all aspects of insurers’ use of data and analytics. Sutherland will continue to closely monitor these developments.