Reconsidering insurance discrimination and adverse selection in an era of data analytics
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Reconsidering insurance discrimination and adverse selection in an era of data analytics David A. Cather1 Received: 6 July 2019 / Accepted: 17 March 2020 © The Geneva Association 2020
Abstract This article demonstrates how replacing age- and gender-based pricing variables with telematics data in auto insurance risk classification systems minimises insurance discrimination and increases cream skimming adverse selection. The study explains how incorporating telematics data into insurance pricing schemes reduces pricing heterogeneity, consistent with the anti-discrimination objective of Aristotelian equality. It also describes how anti-discrimination prohibitions of age- and gender-based insurance pricing can result in regulatory adverse selection; traditional adverse selection, where asymmetric information favouring applicants can result in an overpopulation of high-risk drivers in risk pools; and cream skimming adverse selection, where asymmetric information favouring telematics-based insurers supports premium discounts that attract safer drivers, prompting an underpopulation of low-risk drivers among non-telematics insurers. The study explains how insurers minimise their vulnerability to cream skimming by quickly entering the pricing “arms race” with their own telematics-based products and how incorporating telematics data increases the efficiency of risk classification systems. Keywords Adverse selection · Discrimination · Telematics · Auto insurance
Introduction Critics of the insurance industry often contend that the risk classification process used to sort personal insurance applicants into different pricing categories results in price discrimination. This study examines how new auto insurance risk classification variables based on data analytics can minimise such discrimination without compromising insurance pricing accuracy and how anti-discrimination prohibitions on insurance pricing variables can disrupt insurance markets by causing adverse * David A. Cather [email protected] 1
Risk Management Department, Smeal College of Business, Pennsylvania State University, State College, PA 16802, USA Vol.:(0123456789)
D. A. Cather
selection. Recent developments have expanded efforts to reduce discrimination in employment and in the pricing of consumer goods and services; in both these settings, concern about insurance discrimination is an issue. For example, Article 21 of the European Union’s Charter of Fundamental Rights bans discrimination in a variety of forms, identifying as discriminatory several risk classification variables used to price insurance, including gender and age.1 The European Court of Justice similarly ruled in the 2011 Test-Achats case that information about gender cannot be used in pricing insurance.2 Earlier, in its Manhart and Norris cases, the U.S. Supreme Court banned gender-based pricing in employer-sponsored retirement plans, explaining that employers could not provide women with less protection or charge them higher prices than men in retirement plans even though women live longer t
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