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