Antidiscrimination Insurance Pricing: Regulations, Fairness Criteria, and Models

Abstract

On the issue of insurance discrimination, a grey area in regulation has resulted from the growing use of big data analytics by insurance companies: direct discrimination is prohibited, but indirect discrimination using proxies or more complex and opaque algorithms is not clearly specified or assessed. This phenomenon has recently attracted the attention of insurance regulators all over the world. Meanwhile, various fairness criteria have been proposed and flourished in the machine learning literature with the rapid growth of artificial intelligence (AI) in the past decade and have mostly focused on classification decisions. In this article, we introduce some fairness criteria that are potentially applicable to insurance pricing as a regression problem to the actuarial field, match them with different levels of potential and existing antidiscrimination regulations, and implement them into a series of existing and newly proposed antidiscrimination insurance pricing models, using both generalized linear models (GLMs) and Extreme Gradient Boosting (XGBoost). Our empirical analysis compares the outcome of different models via the fairness–accuracy trade-off and shows their impact on adverse selection and solidarity.

ACKNOWLEDGMENTS

The authors are grateful to the anonymous reviewers and editor for their constructive comments. The authors thank Chris Dolman, Edward (Jed) Frees, and Michael Powers for valuable comments and suggestions and various seminar participants for helpful comments.

Notes

1 Birnbaum (2020b) made a similar point in his presentation to the National Association of Insurance Commissioners Consumer Liaison Committee, and asked, "If discriminating intentionally on the basis of prohibited classes is prohibited—e.g., insurers are prohibited from using race, religion or national origin as underwriting, tier placement or rating factors—why would practices that have the same effect be permitted?”

2 We believe this is a legal term derived from U.S. employment discrimination laws and is synonymous with intentional discrimination.

3 Note that we use a narrow definition of indirect discrimination assuming that the law has prohibited or will prohibit direct discrimination on protected characteristics, and we limit the scope of our research on indirect discrimination to this situation. We recognize that direct discrimination and indirect discrimination on the same protected characteristic may occur simultaneously, but if direct discrimination is allowed, then the provisions on indirect discrimination will be meaningless.

4 Although it may cover intentional indirect discrimination, it is too difficult to prove discriminatory intent under a disparate treatment case.

5 EIOPA (2019) noted that “some insurance firms declared that they ‘smoothed’ the output of such algorithms, for instance by not using machine learning without human intervention or by establishing caps to the outputs of these tools in order to ensure ethical outcomes (e.g. not charging vulnerable customers excessively).⋯ Regarding the potential difficulties to access insurance for high-risk consumers,⋯ motor insurance firms also referred to already existing mechanisms in some jurisdictions such as insurability schemes or the obligation of insurance firms to not reject motor third-party liability insurance (MTPL) consumers (albeit there is no limit in maximum premium).”

6 In December 2021, a new Innovation, Cybersecurity, and Technology (H) Committee was formed to address the insurance implications of emerging technologies and cybersecurity. The Big Data and Artificial Intelligence (H) Working Group is part of the H committee; see NAIC (2021).

7 Previously that, on February 8, 2013, the U.S. Department of Housing and Urban Development (HUD) issued a final rule titled “Implementation of the Fair Housing Act’s Discriminatory Effects Standard” (“the 2013 rule”) that authorizes disparate impact claims under the FHA as a formal interpretation of the Act, consistent with HUD’s long-held view. In particular, HUD restated its position that the FHA applies to homeowners insurance, and hence the disparate impact standard is applicable to prohibit discriminatory insurance practices with regards to homeowners insurance.

8 “Proxy discrimination occurs when insurers discriminate based on facially-neutral traits that (i) are correlated with membership in a protected groups, and (ii) are predictive of losses for precisely that reason”. This definition of “proxy discrimination” was submitted by Professor Daniel Schwarcz to the NAIC during the NAIC’s deliberations regarding its Principles on Artificial Intelligence, based on work in Prince and Schwarcz (2019), available at https://33afce.p3cdn2.secureserver.net/wp-content/uploads/2020/11/Prof.-Dan-Schwarcz-Proxy-Discrimination-Definition.pdf.

9 In pg15training, the first 21 records have been removed because they are duplicate records, which have nonzero claim count (Numtppd) and zero claim amount (Indtppd). After removal, there are exactly 50,000 policies each year in 2009 and 2010.

10 In France, each policyholder is assigned a starting bonus–malus coefficient (a.k.a., coefficient de réduction-majoration in French, abbreviated as CRM) of 1.00 under the French bonus–malus system, and the range of the coefficients extends from 0.50 (i.e., the maximum no-claims bonus will be obtained after at least 13 consecutive claim-free years without a responsible accident) to 3.50. A bonus—malus coefficient is used to adjust the basic premium of the policyholder from the maximum reduction allowed of 50% to the maximum increase allowed of 350%. The French system rewards a claim-free year by a 5% reduction of the coefficient, which is applied to the coefficient for the previous year, and penalizes each fully responsible accident by a 25% increase of the coefficient and each partially responsible accident by half (i.e., 12.5%). More information about bonus-malus in automobile insurance in France can be found on the Directorate of Legal and Administrative Information (2020).

11 Alternatively, gender proxies can be constructed based on variables in the training sample only (Age is highly influential in the gender proxy in this example). However, in this case the developed proxy is ineffective for MU’, as there is no new information added to MU’ compared to MU.

12 We simulate five male binary proxy variables and five female binary proxy variables. For example, in order to simulate the male proxy variable, given the gender of a person, each male has a 60% chance of being in the positive class, while each female only has a 40% chance.

13 Insurance Score: In Scenario 1, we compare MU with MCDP; Density: we compare MCDP’s performance between Scenarios 1 and 2; Age: in Scenario 3, we compare MU with MCDP.

14 Disparate impact discrimination is also applicable under Title VI. According to the U.S. Department of Transportation (n.d.a, n.d.b), “Disparate impact (also called adverse impact) discrimination happens under Title VI when a recipient of federal funds from FHWA adopts a procedure or engages in a practice that has a disproportionate, adverse impact on individuals who are distinguishable based on their race, color, or national origin–even if the recipient did not intend to discriminate” (https://www.fhwa.dot.gov/civilrights/programs/docs/Title/%20VI/%20-/%20Types/%20of/%20Discrimination.pdf). Similarly, for more detail on the three-step approach regarding how to prove a violation of disparate impact standard under the Title VI, see the Title VI Legal Manual published by the U.S. Department of Justice (2021) (https://www.justice.gov/crt/fcs/T6Manual7/#:∼:text=To/%20establish/%20an/%20adverse/%20disparate,and/%20(4)/%20establish/%20causation).

15 Including Wards Cove Packing Co. v. Atonio, 490 U.S. 642 (1989); see also Civil Rights Act of 1991 (1991) §2(2): “The decision of the Supreme Court in Wards Cove Packing Co. v. Atonio, 490 U.S. 642 (1989) has weakened the scope and effectiveness of Federal civil rights protections”; the Wards Cove case’s precedent was nullified by the 1991 Act Because this precedent would make it extremely difficult for the plaintiff to prove disparate impact claims under Title VII.

16 In particular, in Alexander v. Sandoval, (2001), U.S. Supreme Court Held Title VI statute does not allow for private lawsuits based on disparate impact (https://www.fhwa.dot.gov/civilrights/programs/docs/Title%20VI%20-%20Intentional%20Discrimination%20and%20Disparate%20Impact.pdf).

17 See the 2013 rule, HUD responded to the concerns from the insurance industry that “HUD has long interpreted the Fair Housing Act to prohibit discriminatory practices in connection with homeowner’s insurance, and courts have agreed with HUD, including in Ojo v. Farmers Group (2011). Moreover, as discussed above, HUD has consistently interpreted the Act to permit violations to be established by proof of discriminatory effect. By formalizing the discriminatory effects standard, the rule will not, as one commenter suggested, ‘undermine the states’ regulation of insurance.’⋯ McCarran-Ferguson does not preclude HUD from issuing regulations that may apply to insurance policies.”

18 Civil Case No. 13–00966 (RJL), United States District Court, District of Columbia, signed November 7, 2014 (see https://ecf.dcd.uscourts.gov/cgi-bin/show_public_doc?2013cv0966-47; see also The Lawyers' Committee for Civil Rights Under Law (2015) https://www.lawyerscommittee.org/project/aianamic/). “Judge Leon, accepting plaintiffs’ argument that the FHA only prohibits intentional discrimination and that the McCarran-Ferguson Act forecloses the application of disparate impact theory to the provision of homeowners’ insurance, held the FHA unambiguously forecloses the possibility of disparate impact claims.”

19 See a comprehensive summary for the differences in the HUD’s 2020 Rule and how the Inclusive Communities decision in 2015 is different from the HUD’s 2013 Rule (Friedman 2020) (https://www.jdsupra.com/legalnews/hud-issues-final-rule-on-the-fair-63161/).

20 See the Federal Reserve (2017) (https://www.federalreserve.gov/boarddocs/supmanual/cch/fair_lend_over.pdf): “Redlining is a form of illegal disparate treatment whereby a lender provides unequal access to credit, or unequal terms of credit, because of the race, color, national origin, or other prohibited characteristic(s) of the residents of the area in which the credit seeker resides or will reside or in which the residential property to be mortgaged is located. Redlining may violate both the FHAct and the ECOA [Equal Credit Opportunity Act].”