Reconsidering insurance discrimination and adverse selection in an era of data analytics

Cather, David A.

doi:10.1057/s41288-020-00166-7

Cited by 18 publications

(13 citation statements)

References 43 publications

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“…Insurance discrimination is a topic of globally shared legal concepts that are quickly evolving (Cather, 2020). Numerous countries have collectively developed an extensive legal framework to address insurance discrimination, with jurisdictions in North America and the EU taking different approaches to address the issue.…”

Section: State Insurance Discrimination Standardsmentioning

confidence: 99%

“…If applied consistently with the discussion of Figure 1 above, Aristotelian equality may be used to account for the heterogeneity associated with insurance discrimination in a manner that considers differences in expected losses, addressing a key concern about using disparate impact theory in discrimination cases (Miller, 2009;Shapo, 2020). An application of Aristotelian equality is also well suited to an industry whose pricing practices are quickly changing due to data analytics, as the availability of new types of pricing variables has enabled innovative insurers to add and replace pricing variables in risk classification systems to reduce heterogeneity in risk pools (Cather, 2020).…”

Section: Aristotelian Equality and The Eu Ban On Discriminatory Insurance Pricingmentioning

confidence: 99%

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Insurance pricing discrimination and Aristotelian equality: an application to annuity pricing

Cather¹

2021

JIR

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International courts often apply the social justice standard of Aristotelian equality—treating like people alike and unlike people differently—to cases involving insurance pricing discrimination. This article examines whether the use of insurance pricing variables like gender and race results in discriminatory pricing categories consisting of heterogeneous policyowners, in violation of Aristotelian equality. This article applies this discrimination standard to the pricing of annuities, drawing from studies investigating the racial mortality crossover, findings that show that the mortality rate of Black Americans falls below the rate of White Americans at advanced ages. Based on the crossover literature, this study demonstrates how race-based annuity pricing would be discriminatory because it results in heterogeneous pricing within racial pricing categories, but that insurers can control for this heterogeneity by using the wider variety of annuity pricing data (e.g., medical history, diseases, and smoking) developed in the enhanced annuity submarket. The article demonstrates how the increased use of data analytics in insurance pricing to control for heterogeneity is consistent with Aristotelian equality.

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Section: State Insurance Discrimination Standardsmentioning

confidence: 99%

Section: Aristotelian Equality and The Eu Ban On Discriminatory Insurance Pricingmentioning

confidence: 99%

Insurance pricing discrimination and Aristotelian equality: an application to annuity pricing

Cather¹

2021

JIR

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“…Admittedly, there are concerns on using personalized data and algorithmic prediction (Mahapatra 2019;Cevolini and Esposito 2020). Despite this, we prefer the statement by Cather (2020) that incorporating telematics data into automobile insurance risk classification systems would "minimize insurance discrimination and increase cream skimming adverse selection." Specifically, asymmetric information favoring telematics-based insurers supports premium discounts that attract safer drivers, prompting an underpopulation of low-risk drivers among non-telematics insurers.…”

Section: Introductionmentioning

confidence: 99%

Improving Automobile Insurance Claims Frequency Prediction With Telematics Car Driving Data

et al. 2021

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Novel navigation applications provide a driving behavior score for each finished trip to promote safe driving, which is mainly based on experts’ domain knowledge. In this paper, with automobile insurance claims data and associated telematics car driving data, we propose a supervised driving risk scoring neural network model. This one-dimensional convolutional neural network takes time series of individual car driving trips as input and returns a risk score in the unit range of (0,1). By incorporating credibility average risk score of each driver, the classical Poisson generalized linear model for automobile insurance claims frequency prediction can be improved significantly. Hence, compared with non-telematics-based insurers, telematics-based insurers can discover more heterogeneity in their portfolio and attract safer drivers with premiums discounts.

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“…In contrast, low-risk policyholders tend to exit the market and form an unstable insurance market. Researches demonstrate that adverse selection exists widely in crop insurance (Gunnsteinsson, 2020), health insurance (Soika, 2018), and automobile insurance (Cather, 2020), causing substantial economic losses (Cohen & Siegelman, 2010). Besides, Boyer and Peter (2020) have proved adverse selection, and insurance fraud has the potential to aggravate each other, which will accelerate low-risk policyholders dropping out insurance market.…”

Section: Introductionmentioning

confidence: 99%

Policyholder cluster divergence based differential premium in diabetes insurance

Tang

Qin

et al. 2021

Manage Decis Econ

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Traditional health insurance pricing, which is based on experience rates, cannot correctly estimate the risk types of policyholders, can lead to serious adverse selection. Due to massive data volumes and developments in data analysis technology, the underwriting process can more accurately reflect the insured's risk type. Therefore, this paper based on policyholder cluster divergence proposes a differential premium approach by employing fuzzy c‐means algorithm (FCM) with an extended initial multistate Markov model to formulate the differential premium that matches the policyholder's risk category. Our results confirm that the proposed differential premium approach better reveals the policyholder's risk type as compared with unified pricing and effectively counteracts adverse selection.

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Reconsidering insurance discrimination and adverse selection in an era of data analytics

Cited by 18 publications

References 43 publications

Insurance pricing discrimination and Aristotelian equality: an application to annuity pricing

Insurance pricing discrimination and Aristotelian equality: an application to annuity pricing

Improving Automobile Insurance Claims Frequency Prediction With Telematics Car Driving Data

Policyholder cluster divergence based differential premium in diabetes insurance

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