Privacy Concerns in Insurance Markets: Implications for Market Equilibria and Social Welfare

Gemmo, Irina; Browne, Mark J.; Gründl, Helmut

doi:10.2139/ssrn.3480629

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…At its core, our model concerns competition between conventional insurers who employ a legacy underwriting technology and new tech entrants who employ a new technology 5 Reinganum (1981) considers a new technology which is superior to extant technology in both costs and quality, and analyzes the diffusion process of such new technology in reshaping the market structure. Instead of exploring the cost-information tradeoff, extant literature models this relationship in a simplified way assuming either that the information asymmetry and thus adverse selection is eliminated at a cost (Thakor, 1982) or that the technology has zero cost (Filipova-Neumann & Welzel, 2010;Gemmo et al, 2019). Browne and Kamiya (2012) analyze a Wilsonian framework wherein the policyholder decides whether the insurer can use the technology during the underwriting process.…”

Section: Institutional Backgroundmentioning

confidence: 99%

“…The subsequent costs to maintain the technology can also be substantial and might be both of variable and fixed nature, depending on whether the technology is self-operated or outsourced to a technology partner. The cost of new technology may also be interpreted as individuals' transparency costs (Gemmo et al, 2019), where people do not like being monitored, that is, a type of intangible cost for tech insurers.…”

Section: Introductionmentioning

confidence: 99%

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Technology heterogeneity and market structure

Eling

Jia

Lin

et al. 2021

J of Risk & Insurance

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New technologies may allow principals or firms to learn more quickly about the characteristics of their agents or clients-at some cost of employing that technology. We study the implications of this speedversus-cost tradeoff for equilibrium pricing and purchasing decisions in insurance markets featuring adverse selection. In particular, we study dynamic competitive equilibrium in a theoretical model featuring individuals who differ in their privately known risk types and featuring two types of insurers: conventional insurers who employ a legacy learning technology and tech insurers who employ a new technology. Equilibrium in our model features sorting of low-risk types into tech firms and high-risk types into conventional firms. We show, intuitively, that lowering the technology cost raises this cutoff and thus increases the equilibrium market share of tech firms. Perhaps counter-intuitively, however, we show that adverse selection effects within the conventional market can cause an attempt by conventional insurers to catch up with tech firms-by increasing the speed at which they learn about the risk types of their insureds-to backfire and lead to an increase in the market share of tech

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Section: Institutional Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Technology heterogeneity and market structure

Eling

Jia

Lin

et al. 2021

J of Risk & Insurance

View full text Add to dashboard Cite

show abstract

“…We also refer to the ongoing discussion on transparency aversion in insurance markets, which highlights the potential negative consequences of transparency on insurance demand (Gemmo et al , 2019). Notable here is the paradox between expressed privacy concerns and actual behavior [for a literature review see Barth and de Jong (2017)].…”

Section: Analysis Of Insurabilitymentioning

confidence: 99%

The impact of telematics on the insurability of risks

Eling

Kraft

2020

JRF

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Purpose The purpose of this paper is to analyze the use of telematics in insurance and its consequences for the insurability of risks. Empirical results on monitoring policyholders or insured objects and its consequences for asymmetric information, as well as claims frequency and severity are discussed. Furthermore, potential future research questions that arise from the use of telematics in risk management and insurance are outlined. Design/methodology/approach The paper systematically reviews existing studies and then investigates the consequences of telematics using Berliner’s insurability criteria. The results are based on 52 academic studies and industry papers published from 2000 to 2019. Findings The findings emphasize the effects of new information on information asymmetry and risk pooling, the implications of new technologies on loss frequency and severity, legal restrictions and ethical consequences of the use of telematics in the insurance field. Problems with the insurability impede the market development of innovations such as telematics tariffs. Originality/value Despite its increasing relevance for businesses at present, research on telematics in insurance is limited. Some papers can be found in the IT domain, but relatively little research has been done in the business and economics literature. The authors illustrate where the research stands currently and outline directions for future research.

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“…Our work is also related to the literature on contract form as a screening mechanism. This literature focuses on risk classification through consumers' choices between mutual and stock insurance contracts (Ligon and Thistle, 2005;Smith and Stutzer, 1990), full and limited tort coverage (Posey and Thistle, 2017) as well as regular and transparency contracts based on wearables or telematics devices (Gemmo et al, 2017).…”

mentioning

confidence: 99%

Risk classification with on‐demand insurance

Braun

Haeusle

Thistle

2023

J of Risk & Insurance

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On‐demand insurance is an innovative business model from the InsurTech space, which provides coverage for episodic risks. It makes use of a simple fact in a practical way: People differ in their frequency of exposure as well as the probability of loss. The extra dimension of heterogeneity can be used to screen the insured and shifts the utility‐possibility frontier outward. We provide a sufficient condition under which type‐specific full insurance at the actuarially fair price is incentive compatible. We also show that our results hold for various real‐world implementations of on‐demand insurance.

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Privacy Concerns in Insurance Markets: Implications for Market Equilibria and Social Welfare

Cited by 8 publications

References 31 publications

Technology heterogeneity and market structure

Technology heterogeneity and market structure

The impact of telematics on the insurability of risks

Risk classification with on‐demand insurance

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