Life insurance: genomic stratification and risk classification

Joly, Yann; Burton, Hilary; Knoppers, Bartha Maria; Feze, Ida Ngueng; Dent, Tom; Pashayan, Nora; Chowdhury, Susmita; Foulkes, William D.; Hall, Alison; Hamet, Pavel; Kirwan, Nick; Macdonald, Angus S.; Simard, Jacques; Hoyweghen, Ine Van

doi:10.1038/ejhg.2013.228

Cited by 47 publications

(38 citation statements)

References 28 publications

(36 reference statements)

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“…Ideally, it would be possible to distinguish genetic research findings and clinical test results for the purposes of risk assessment; however, this is difficult in translational research. The exclusion of all research findings is thus supported by the Human Genetics Society of Australasia [29] and an international body of experts [15] . It also reflects international practice, such as arrangements for participants in the UK's 100,000 Genomes project [17,30] .…”

Section: Concerns Around the Current Policy Climatementioning

confidence: 99%

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Genetics and Insurance in Australia: Concerns around a Self-Regulated Industry

Newson

Tiller

Keogh

et al. 2017

Public Health Genomics

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Background: Regulating the use of genetic information in insurance is an issue of ongoing international debate. In Australia, providers of life and other mutually rated insurance products can request applicants to disclose all results from any genetic test. Insurers can then use this information to adjust premiums and make policy decisions. The Australian Financial Services Council (FSC; an industry body) developed and maintains the relevant industry standard, which was updated in late 2016. Aims/Objective: To review the 2016 FSC Standard in light of relevant research and determine the legitimacy of the Australian regulatory environment regarding use of genetic information by insurers. Results: We identified five concerns arising from the 2016 FSC Standard: (1) use of results obtained from research; (2) the requirement for an applicant to disclose whether they are “considering” a genetic test; (3) failure to account for genome sequencing and other technology developments; (4) limited evidence regarding adverse selection; and (5) the inappropriateness of industry self-regulation. Conclusion: Industry self-regulation of the use of genetic information by life insurers, combined with a lack of government oversight, is inappropriate and threatens to impede the progress of genomic medicine in Australia. At this critical time, Australia requires closer government oversight of the use of genetic information in insurance.

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Section: Concerns Around the Current Policy Climatementioning

confidence: 99%

“…Insurance implications are also a concern for many individuals considering participating in medical research involving return of genetic findings, and can be a significant reason for declining to participate [12][13][14][15] .…”

mentioning

confidence: 99%

Genetics and Insurance in Australia: Concerns around a Self-Regulated Industry

Newson

Tiller

Keogh

et al. 2017

Public Health Genomics

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“…The HGSA has been proactive in urging the insurance industry to not require disclosure of genetic testing undertaken as part of a research project as it may impact upon research participation and to implement a moratorium on the use of predictive genetic information pending improved actuarial estimates of the impact of such information on adverse selection [31]. Similar concerns were identified at a meeting of multidisciplinary experts in Quebec, Canada in 2012, and a number of recommendations were made for best practice going forward [24]. In regard to these, the following summarises where Australia is currently placed: (1) That life insurance policies be available covering a minimal (ceiling) amount at an affordable rate and with no health questions asked (including about genomics) is partially addressed at a limited level through the Australian retirement Superannuation system [32].…”

Section: Resultsmentioning

confidence: 99%

How are genetic test results being used by Australian life insurers?

et al. 2018

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In Australia, the USA and many Asian countries the life insurance industry is self-regulated. Individuals must disclose genetic test results known to them in applications for new or updated policies including cover for critical care, income protection and death. There is limited information regarding how underwriting decisions are made for policies with such disclosures. The Australian Financial Services Council (FSC) provided de-identified data collected on applications with genetic test result disclosure from its life insurance member companies 2010–2013 to enable repetition of an independent examination undertaken of applications 1999-2003: age; gender; genetic condition; testing result; decision-maker; and insurance cover. Data was classified as to test result alone or additional other factors relevant to risk and decision. Where necessary, the FSC facilitated clarification by insurers. 345/548 applications related to adult-onset conditions. The genetic test result solely influenced the decision in 165/345 applications: positive (n = 23), negative (n = 139) and pending (n = 3). Detailed analyses of the decisions in each of these result categories are presented with specific details of 11 test cases. Policies with standard decisions were provided for all negative test results with evidence of reassessment of previous non-standard decisions and 20/23 positive results with recognition of risk reduction strategies. Disclosure of positive results for breast/ovarian cancer, Lynch syndrome and hereditary spastic paraplegia, and three pending results, generated non-standard decisions. The examination demonstrates some progress in addressing concerns in regard to utilisation of genetic test information but the self-regulatory system in Australia only goes some way in meeting internationally recommended best practice.

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“…The big data technologies revolutionize the way insurance companies to collect, process, analyze, and manage data more efficiently [1,2]. Thus, proliferate in various sectors of insurance industries such as risk assessment, customer analytics, product development, marketing analytics, claims analysis, underwriting analysis, fraud detection, and reinsurance [3,4].…”

Section: Introductionmentioning

confidence: 99%

Risk prediction in life insurance industry using supervised learning algorithms

Boodhun

Jayabalan

2018

Complex Intell. Syst.

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Risk assessment is a crucial element in the life insurance business to classify the applicants. Companies perform underwriting process to make decisions on applications and to price policies accordingly. With the increase in the amount of data and advances in data analytics, the underwriting process can be automated for faster processing of applications. This research aims at providing solutions to enhance risk assessment among life insurance firms using predictive analytics. The real world dataset with over hundred attributes (anonymized) has been used to conduct the analysis. The dimensionality reduction has been performed to choose prominent attributes that can improve the prediction power of the models. The data dimension has been reduced by feature selection techniques and feature extraction namely, Correlation-Based Feature Selection (CFS) and Principal Components Analysis (PCA). Machine learning algorithms, namely Multiple Linear Regression, Artificial Neural Network, REPTree and Random Tree classifiers were implemented on the dataset to predict the risk level of applicants. Findings revealed that REPTree algorithm showed the highest performance with the lowest mean absolute error (MAE) value of 1.5285 and lowest root-mean-squared error (RMSE) value of 2.027 for the CFS method, whereas Multiple Linear Regression showed the best performance for the PCA with the lowest MAE and RMSE values of 1.6396 and 2.0659, respectively, as compared to the other models.

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Life insurance: genomic stratification and risk classification

Cited by 47 publications

References 28 publications

Genetics and Insurance in Australia: Concerns around a Self-Regulated Industry

Genetics and Insurance in Australia: Concerns around a Self-Regulated Industry

How are genetic test results being used by Australian life insurers?

Risk prediction in life insurance industry using supervised learning algorithms

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