Rethinking Underwriting: Fair vs. Unfair Assessment in Changing Industry Landscape

1. FAIR vs. UNFAIR UNDERWRITINGRethinking Our Approach Hank George, FALU

2. The content of this presentation has changed significantly from the original version that was prepared for a concurrent session at the AHOU conference in New Orleans. Other than the presenter, no one has had any input in the content of today’s webinar. Therefore, all viewpoints and opinions expressed in this webinar are solely those of Hank George, FALU

3. Because of 7 major trajectories impacting our industry, it is time for an objective assessment of where we stand on matters involving fair vs. unfair life underwriting

4. New York DFS January 18 letter • Expectations of millennial/post-millennial customers • Changing demographics in our population • Intense emphasis on reducing operating expenses • “Big Data” predictive analytics at our fingertips • Potential impact of artificial intelligence • Consumer perceptions of our industry

5. “The DFS’s latest guidance indicates that not only is it closely monitoring compliance with underwriting rules, but also that it believes that insurers may run afoul of the law by using criteria that have a ‘disparate impact’ on protected [classes]. This development highlights the need for companies to review their underwriting models to ensure that they are actuarially sound anddo not unfairly discriminate against a protected class.”“InsurTech Alert” from Akin, Gump, Strauss, Haure and Feld, LLPJanuary 30, 2019

6. Protected Classes in Underwriting Race/Color Ethnicity Creed (religion) Sexual Orientation Victims of Domestic Violence

7. Two kinds of Unfair Discrimination Disparate Treatment Disparate Impact Treatment on the basis of inadequately justified factors –not including membership in a protected class per se– that nevertheless disadvantage members of a protected class Differential treatment on the basis of membership in a protected class that disadvantages members of that class

8. “Information about a person’s physical activity, income, race/ethnicity and neighborhood…collected and traded online is increasing exponentially.” I. Glenn Cohn, JDHarvard Law School“Preserving Privacy and the Big Data Enterprise are on a Collision Course”Journal of the American Medical Association320,3 (July 17, 2018):231[editorial]

9. “A risk selection solution not properly tested and vetted runs a very real risk of unintentionally targeting a certain population.” Dianne Schuetz et alRGA ReOn The Risk34,4 (December 2018):68

10. In the last few years, a number of ostensible underwriting resources have been introduced that have raised questions about disparate discrimination. When touting the putative advantages of these resources, their advocates have sometimes exaggerated what they consider to be drawbacks of conventional underwriting resources. Here are some examples…

11. Medical records are ordered on a ridiculous percentage of cases • Blood chemistry used in underwriting is not nearly as reliable as we think it is. • An invasive paramedical exam takes several weeks to schedule and administer. • Underwriting solutions using third-party data integration will potentially exclude the medical underwriting component • We’re looking at ways to completely eliminate fluids in underwriting. • Life insurance buyer dissatisfaction is due to its less appealing elements: blood draws, urine samples, medical questionnaires Note: references available upon request

12. The following data elements were endorsed for use in life underwriting in an article in a major actuarial publication: • Credit Score • Facial Image Analysis (“selfies”) • Zip Code • Food Preferences • Psychological and Emotional Health from Wellness Websites and Programs

13. In 2018, another publication cited 3 resources as having the “…potential for transformative change [and] barring regulatory interference, it is only a matter of time before insurance companies will make use of these data.” • Facial Recognition (“selfies”) • Location Tracking (GPS) • Social Media to Predict Mental Health Issues

14. What is the consumer perspective?

15. Insurers are making risk assessments… “…without public scrutiny, based on data about things like race, marital status, how much TV you watch, whether you pay your bills on time and even buy plus-size clothing.” Marshall AllenNational Public RadioJuly 17, 2018

16. The Insurance Companies Using Data survey asked 1000 adults for their views on insurer use of advanced data: ✓ 72% said insurers should not be allowed to use Big Data to determine insurability ✓ 71% would not let insurers have access to their DNA in order to get a lower premium ✓ The majority had noproblems with insurers using medical histories, blood work, etc.

17. “Data-driven decisions have the potential to negatively impact already disadvantaged populations, despite the fact that they might seem fairer. Such decisions can reinforce existing disparities, prompting concern for a ‘disparate impact.’ The data scientists building such models are often in the dark and may be unintentionally creating an unfair model.” Brian d’Alessandro“A Data Scientist’s Guide to Discrimination-Aware Classification”Big Data5,2(2017):120

18. Redlining The “Poster Child” for Disparate Discrimination in Life Underwriting

19. “In Durham…your life expectancy depends on your 5-digit ZIP code. A legacy of segregation in housing and other factors have contributed to an 11-year differential in life expectancy across Durham’s ZIP codes, ranging from 75 to 86.” Katie F. Huffman, et alDuke University Medical SchoolJournal of the American Geriatrics Society66(2018):25

20. “…as a complement to medical sources of data we are now able to use a credit-based mortality score as a predictive tool that represents the behavioral side of risk.” Dianne Schuetz et alOp Cit.

21. One prominent algorithm uses 4 credit attributes as part of a larger risk score: • Open credit accounts • Total balance in all active accounts • Amount of post-due balances • Number of collections Industry studies have documented significant excess mortality in individuals with unfavorable credit attributes.

22. “Our current credit-scoring systems have a disparate impact on people and communities of color. These systems are rooted in our long history of housing discrimination and the dual credit market that resulted from it.” Lisa Rice“Discriminatory Effects of Credit Scoring on Communities of Color”Suffolk University Law Review46(2013):935

23. According to a Duke University study: “life insurance companies that acquire an applicant’s credit score are also indirectly acquiring information about that applicant’s educational attainment, intelligence and personality, right back to childhood.” They concluded that these characteristics are not legitimate grounds for screening individuals.1In a Philadelphia study, the median credit score was 132 points higher in predominantly Caucasian areas vs. non-Caucasian areas.21 Israel. PNAS. 111(December, 2014):17087. 2 Ratcliffe. “Financial Health of Residents” Philadelphia. The Urban Institute. 2017

24. In an Illinois ZIP code study 54.2% of African Americans and 16.8% of Caucasians had a credit score ≤ 25th percentile.1In a John Hopkins study credit scores were negatively correlated with the % of African American residents.2 In a Dartmouth study there was a highly significant link between Zip Codes and credit delinquency3 1 Duda. Woodstock Institute. August, 2010 2 Dean. Journal of Epidemiology and Community Health. 73(2019):73 3 Houle. American Journal of Public Health. 105(2015):e75

25. Facial imaging for the alleged determination of biological vs. chronological age – using “selfies” –is being promoted as an underwriting resource.Given extensive evidence from research studies, does this constitute unfair disparate discrimination?

26. “Face age-estimation is wrought with many challenging interactions that cannot easily be separated out. In general, aging patterns are well understood for all humans, however, these patterns become confounded by intrinsic factors of genetics, gender differences, and ethnic derivations…” Karl Ricanek Jr., PhD University of North Carolina Wilmington Cofounder of Lapetus Solutions Generalized Multi-Ethnic Face Age-Estimation IEEE Conference September, 2009

27. “Face morphology is more strongly determined by genetic factors than environmental factors.” Seongwon Chu, MDKyung Hee University Medical School, SeoulBMC Genetics19(2018):481 4 additional studies confirm this finding: Liu PLoS Genetics 8(2012):e1002932 Shaffer. PLoS Genetics. 12(2016):e1006149 Leong. Facial & Plastic Surgery Clinics of North America. 16(2008):277 Roosenboom. Biomedical Research International. 2016:3054519

28. Facial imaging exposes applicant to potential latent racial discrimination (we do not ask about race on insurance applications) ✓Facial imaging is inherently biased against those who do not have white skin1 ✓Facial features were shown to be highly predictive of sexual orientation2 ✓In the 2019 Life Underwriting Requirements study, none of the 91 participating insurers used “selfies” in life underwriting 1 “What machines can tell from your face” The Economist 9/9/17 2 Kosinski. Journal of Personality and Social Psychology. 114,2(2018):246

29. Genetic Testing and the Use of Genetic Test Results in Life Underwriting

30. “…with permission to access individuals’ medical records, life and long-term care insurers will use genetic information to avoid adverse selection whenever possible. Accordingly, clinical genetic tests open patients up to legally allowable discrimination.” Abraham P. Schwab et al“Genomic Privacy”Clinical Chemistry64,12(2018):1696

31. In February, I asked Dr. Jeff Grubbe, Chair of the ACLI Medical Section, if he was aware of any stated industry policy or standard regarding the use of genetic testing or genetic information in life underwriting.Jeff was not aware of any such policy or standard. He said the discussions he had sat in on supported the notion that it is appropriate for insurers to set their own guidelines and policy.Which is precisely what they do…

32. 2018 Critical Issues in Underwriting Survey Question #29 - 93 Respondents“If an APS revealed that a premenopausal female applicant has recently tested positive for the BRCA 1 breast cancer gene mutation and nothing further has been done clinically, would this scenario be either rated or declined based on your underwriting guidelines?”Yes: 49.5%No: 50.5%

33. “Going forward, life insurers need to be proactive to clear up the misconceptions among regulators and applicants regarding how they use genetic information.” Dave Rengachary, MD Chief Medical Director RGA Re “The Time is Now”

34. “In the future, because of the relatively low underwriting benefit and high social cost it is quite likely that the United States will join the list of countries not permitting the use of genetic test results in underwriting life insurance. The only questions are when this more thoughtful approach will be adopted and whether it will be implemented voluntarily by the life insurance industry or imposed by government.” Mark A. Rothstein“Time to End the Use of Genetic Test Results in Life Insurance Underwriting”University of Louisville Institute for Bioethics, Louis D. Brandeis School of Law Journal of Law, Medicine and Ethics 46,3(2018):794-801

35. Did you know that we are the only major western life insurance market without a stated policy on genetic testing and the use of genetic test results in underwriting?

36. EPIGENETIC TESTING Epigenetic scores primarily reflect health habits and other modifiable risk factors, unlike genetic tests that are based on one’s inherited genetic endowment. Two firms are currently developing epigenetic testing methods that may be suitable for use as mortality markers, in conjunction with existing screening assets.

37. EpilogueContemporary Observations

38. A survey by Ernst & Young found that levels of consumer trust in the “insurance sector” consistently ranked below other industries …and were joined at the bottom by pharmaceutical companies!

39. “If there has been a lesson in our short, inconstant flirtation with big data, it is that high-volume processing of personal information rarely rewards consumers in the end.” Nathan Heller The New Yorker February 26, 2019

40. “Consumers will be increasingly more discriminating about the firms they choose to do business with [and] companies that demonstrate ethics, authenticity, and a commitment to social good will see increased consumer loyalty.” Scott R. Kallenbach, FLMI LIMRA Director of Strategic Research LOMA Resource January 2019:26

41. “Clearly, there is an imperative to use data in an ethical and judicious way that consumers trust and feel confident in.” Michael Ferik, FSA Executive Vice President and CFO Guardian Life LOMA Resource January 2019:16

42. Thanks to the New York Department of Financial Services, we are now obliged to carefully consider whether there is any unfair/disparate discrimination inherent in a number of recently introduced life underwriting resources. Will we take the High Road or the Low Road? This decision will profoundly affect how our industry is perceived by consumers!

43. Thank you for your kind attentionLive Long and Prosper!Hank