The Discriminating (Pricing) Actuary

Figures & data

Table 1 Genetic Testing Prohibitions by Country

Regulation category	Country
No regulation	China, Finland, India, Spain, United States
No regulation with written or unwritten codes of conduct from insurance industry groups	Greece, Japan
Prohibitions on insurers requiring applicants to take a genetic test and prohibitions on discrimination if the applicant refuses to take a test	Australia^a
Prohibitions or moratoriums on using results from existing tests when policies are below certain limits	Germany, the Netherlands, Switzerland, United Kingdom
Prohibitions or moratoriums on using results from existing tests at all, sometimes including use of family history information	Austria, Belgium, Canada, Denmark, France, Ireland, Poland Portugal, Singapore

Source: Klein (2017)

Note: ^aAs noted in Vukcevic and Chen (2018), Australia has moved from the third (middle) category to the fifth (bottom) recently.

Table 2 Gamma Regression Model 1 Summary

	Estimate	SE	t Value	Pr(>|t|)
(Intercept)	7.984	0.093	85.580	0.000
VehValue	–0.012	0.022	–0.521	0.602
DrivAgeold people	–0.411	0.104	–3.946	0.000
DrivAgeolder work. people	–0.292	0.093	–3.134	0.002
DrivAgeoldest people	–0.353	0.119	–2.967	0.003
DrivAgeworking people	–0.293	0.093	–3.144	0.002
DrivAgeyoung people	–0.199	0.096	–2.073	0.038
Female	–0.179	0.052	–3.450	0.001

Figure 1. Distribution of Claim Amounts.

Table 3 Gamma Regression Model Summary

	M.1 Coef	M.1 t	M.2 Coef	M.2 t	M.3 Coef	M.3 t	M.4 Coef	M.4 t	M.5 Coef	M.5 t
(Intercept)	7.98	85.58	7.88	89.45	7.61	156.37	8.16	50.19	7.89	54.92
VehValue	–0.01	–0.52	0.00	–0.13	0.00	–0.14	–0.02	–0.88	–0.02	–0.91
DrivAge.old people	–0.41	–3.95	–0.42	–4.01	–0.43	–4.06	–0.45	–4.22	–0.45	–4.26
DrivAge.older work people	–0.29	–3.13	–0.31	–3.24	–0.30	–3.19	–0.30	–3.18	–0.30	–3.14
DrivAge.oldest people	–0.35	–2.97	–0.34	–2.85	–0.35	–2.93	–0.38	–3.13	–0.39	–3.21
DrivAge.working people	–0.29	–3.14	–0.32	–3.39	–0.31	–3.27	–0.30	–3.21	–0.29	–3.11
DrivAge.young people	–0.20	–2.07	–0.21	–2.20	–0.21	–2.16	–0.22	–2.25	–0.21	–2.22
Female	–0.18	–3.45	NA	NA	NA	NA	–0.42	–2.08	–0.43	–2.12
Akaike information criterion	79,365.63	NA	79,391.08	NA	79,390.49	NA	79,382.40	NA	79381.38	NA

Figure 2. Box Plots of Fitted Claim Amounts by Model and Gender.

Table 4 Comparison of Means by Predictors and Gender

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Male	2,215.56	2,011.80	2,013.42	2,045.96	2,050.74	2,007.63
Female	1,863.01	2,015.06	2,013.85	1,989.57	1,986.00	2,018.17

Klein, R. 2017. Genetics and life insurance. A view into the microscope of regulation. Geneva, Switzerland: The Geneva Association. https://www.genevaassociation.org/research-topics/global-ageing/genetics-and-life-insurance-view-microscope-regulation.

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Vukcevic, D., and J. Chen. 2018. Advances in genetics and their impact on life insurance. https://www.actuaries.asn.au/Library/Events/FSF/2018/VukcevicChenPaper.pdf.

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