The Price Is Right? Predictive Modeling, Big Data, and Price Optimization : Regulatory and Litigation Developments

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1 Eric A. Arnold Thomas W. Curvin Mary Jane Wilson-Bilik David Hancock Tracey K. Ledbetter May 12, 2015 The Price Is Right? Predictive Modeling, Big Data, and Price Optimization : Regulatory and Litigation Developments INSURANCE AND FINANCIAL SERVICES LITIGATION WEBINAR SERIES

2 2 Source: Data Brokers: A Call For Transparency and Accountability (FTC May 2014)

3 Predictive Modeling in the Insurance Industry Due to rapid improvements in computation power, data storage capacity, and statistical analysis techniques, over the last several decades predictive modeling has come into widespread use by corporations looking to gain a competitive advantage. Property and Casualty insurers have used predictive models to support underwriting, marketing, and fraud prevention. Life insurers are catching up. 3

4 Life Sector: Data and Modeling Underwriting Use algorithm to approximate mortality risk Additional underwriting & investigation Quick approval and rating Marketing Highest rated customers Most likely purchasers In-force Management Monitor changing mortality risk Inform reinsurance decisions Focus retention efforts Inform changes in nonguaranteed elements 4

5 What Is Price Optimization? Many definitions reflecting different judgments and practices CAS Task Force White Paper (May 7, 2015): identifies seven definitions Ohio Department of Insurance Bulletin : an insurer s practice of varying premiums based upon factors that are unrelated to risk of loss in order to charge each insured the highest price that the market will bear Earnix: a statistical technique to help an insurer determine a rate plan that better fits the competitive environment and corporate goals, within actuarial and regulatory standards 5

6 Consumer Concerns My premiums will go up based on some mysterious black box that uses information that doesn t seem to have anything to do with how risky I am. Actuarial Soundness I will be forced to shop for insurance continually if loyal customers like me will be penalized for staying with the same company. Elasticity of Demand / Actuarial Unfair Discrimination Poorer people and minorities will be especially hurt if their ability to shop around is limited. Covert Suspect-Class Discrimination 6

7 Regulatory Action State Insurance Department Initiatives: Maryland Insurance Administration Bulletin (10/31/14) Ohio Department of Insurance Bulletin (1/29/15) California Department of Insurance Notice (2/18/15) New York 308 Inquiry (3/18/15) MI, WA, MN, ME also reviewing MD, OH, CA: Claim that Price Optimization is unfairly discriminatory and require insurers to cease the practice 7

8 Regulatory Action NAIC -- Casualty Actuarial and Statistical (C) Task Force Draft White Paper (5/7/15) Acknowledges that Price Optimization is largely a matter of applying concrete data and analysis to final pricing decisions that historically were left to anecdotal reasoning and subjective judgment. Appears open to Price Optimization on a theoretical level, especially in face of seemingly categorical rejections by certain departments. 8

9 Recent Class Actions Putative class actions filed in California, Washington Allege that Defendants used price optimization to charge based on market factors rather than risk Allege that Plaintiffs paid more for their insurance than other insureds who presented the same risk, because Plaintiffs were loyal customers and therefore judged to be less price sensitive Assert violations of unfair and deceptive trade practices laws; violations of insurance code; violations of false advertising laws; and unjust enrichment 9

10 Recent Class Actions Actions were removed to federal court under CAFA CA: Voluntarily dismissed without prejudice WA: Motion to dismiss pending Filed Rate Doctrine Failure to exhaust administrative remedies Deference to administrative agency s primary jurisdiction Failure to state a claim 10

11 Anti-Discrimination Laws Laws forbidding suspect class discrimination Laws forbidding discrimination between individuals of the same class and equal expectation of life Laws barring rate classifications for factors other than risk of loss 11

12 Suspect Class Discrimination Information used in predictive modeling cannot be a proxy for information forbidden in insurance underwriting generally State-level differences must continue to be observed in areas such as the use of sex as an underwriting criteria, the use of genetic information, and the inclusion of sexual orientation in the universe of protected classes 12

13 Same Class (Actuarial) Discrimination A fundamental principle of insurance underwriting, pricing, reserving, and the ability to withstand regulatory examination is that like risks should be treated alike. Consumer groups have highlighted inconsistent treatment of similarly situated persons. Consumers will not necessarily understand why the inputs and algorithms of predictive modeling should be relevant to the final price they pay for insurance. The insurance industry must be able to address concerns that come from moral and political points of view, and not just speak a dispassionate language of metrics and correlations. 13

14 Fair Credit Reporting Act (FCRA) Covers More Than Credit FCRA governs the use in insurance underwriting of information supplied by consumer reporting agencies. FCRA is designed to protect the privacy of information obtained in consumer reports ( CR ) and to guarantee the information supplied to the insurer on the CR is accurate and transparent 14 Includes information about a person s credit history, medical condition, driving record, criminal activity Applies in any instance where information in a CR causes a consumer to pay more for coverage, or to receive less coverage, than the consumer would have paid or received if the CR had not been considered Enforced by the Consumer Financial Protection Board

15 FCRA Gives Rights to Consumers To know the contents of their consumer reports To correct misinformation in their consumer reports To control who has access to their consumer reports To know what specific information in a consumer report resulted in a declination of coverage or a higher price/lower limit for coverage FCRA excludes most first parties that collect consumer information. Does not cover IoT devices Does not cover the aggregation of data 15

16 FCRA and Price Optimization Regulators have expressed concerns around: Lack of transparency: Consumer data collected from third parties (not from CR agencies) is often collected without the consumer s knowledge and sources are not clear Negative inferences about consumers: Third party data (not from CR agencies) may be used to make negative, sensitive inferences Inaccessible: If errors, customers can t correct the problem How to de-risk the initiatives? Auditable, screened data Clarity around the nature of consent required for the data usage 16

17 Regulators Concerns with Internet of Things Internet of Things: Privacy & Security in a Connected World, Federal Trade Commission Staff Report (1/15) Security Risks: unauthorized access and misuse of personal information and safety risks Patch known vulnerability over life-cycle of product Privacy Risks: sensitive personal data and behavioral patterns might be collected Concern that it could used to make credit, insurance and employment decisions without the customer s knowledge Data minimization: FTC staff -- principle of privacy protection Notice and Choice 17

18 State Level Information Practices Acts FCRA has specified preemptive effects on state law (see 15 U.S.C. 1681t), but not all state consumer information statutes are preempted by the FCRA. Some states have acts that impose detailed requirements on the collection and use of non-credit consumer information by life insurers. See, e.g., California Investigative Consumer Reporting Agencies Act (Cal. Civ. Code ) 18

19 Federal and State Privacy Acts Possible application of federal and state consumer privacy laws, including: HIPAA Gramm-Leach Bliley Act California s SB-1 (Cal. Fin. Code ) Legal, contractual (Terms of Use), and reputational issues even where consumers have no legal right of privacy Increasing CFPB oversight? 19

20 Questions? Eric A. Arnold Washington, DC Thomas W. Curvin Atlanta, GA Mary Jane Wilson-Bilik Washington, DC David Hancock Special Counsel Tracey K. Ledbetter Atlanta, GA

Real Life Minority Report? Life Insurers Grapple With Big Data s Implications

Eric Arnold, Sutherland Doug Morrin, Prudential Eric Myers, AIG Consumer Insurance Mary Jane Wilson-Bilik, Sutherland October 13, 2015 Real Life Minority Report? Life Insurers Grapple With Big Data s Implications