Fairness in Uncertainty: Some Limits and Misinterpretations of Actuarial Fairness

Frezal, Sylvestre; Barry, Laurence

doi:10.1007/s10551-019-04171-2

Cited by 26 publications

(13 citation statements)

References 36 publications

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“…This questions the role of insurance in society, transitioning from pooling risks (wealth redistribution) to the transfer of individual risks by paying a premium proportional to the actual hazard. See Barry (2019); Keller et al (2018); Frezal and Barry (2019); Barry and Charpentier (2020); Cevolini and Esposito (2020) for an overview of this debate.…”

Section: Outlook On Pricingmentioning

confidence: 99%

Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Blier-Wong

Cossette

Lamontagne

et al. 2020

Risks

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In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, only in the past few years have they begun studying these newer algorithms to tackle insurance-related tasks. In this work, we aim to review the applications of machine learning to the actuarial science field and present the current state of the art in ratemaking and reserving. We first give an overview of neural networks, then briefly outline applications of machine learning algorithms in actuarial science tasks. Finally, we summarize the future trends of machine learning for the insurance industry.

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Section: Outlook On Pricingmentioning

confidence: 99%

Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Blier-Wong

Cossette

Lamontagne

et al. 2020

Risks

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“…To understand what is unfair discrimination and what insurers should be prohibited from doing, let us start with a discussion of "fair" insurance mechanisms. We begin with a historical context, drawing on the work of Frezal and Barry (2019).…”

Section: Social Justice Considerations 21 What Is Actuarial Fairness?mentioning

confidence: 99%

The Discriminating (Pricing) Actuary

Frees¹,

Huang

2021

North American Actuarial Journal

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The insurance industry is built on risk classification, grouping insureds into homogeneous classes. Through actions such as underwriting, pricing, and so forth, it differentiates, or discriminates, among insureds. Actuaries have responsibility for pricing insurance risk transfers and are intimately involved in other aspects of company actions and so have a keen interest in whether or not discrimination is appropriate from both company and societal viewpoints. This article reviews social and economic principles that can be used to assess the appropriateness of insurance discrimination. Discrimination issues vary by the line of insurance business and by the country and legal jurisdiction. This article examines social and economic principles from the vantage of a specific line of business and jurisdiction; these vantage points provide insights into principles. To sharpen understanding of the social and economic principles, this article also describes discrimination considerations for prohibitions based on diagnosis of COVID-19, the pandemic that swept the globe in 2020.Insurance discrimination issues have been an important topic for the insurance industry for decades and are evolving in part due to insurers' extensive use of Big Data; that is, the increasing capacity and computational abilities of computers, availability of new and innovative sources of data, and advanced algorithms that can detect patterns in insurance activities that were previously unknown. On the one hand, the fundamental issues of insurance discrimination have not changed with Big Data; one can think of credit-based insurance scoring and price optimization as simply forerunners of this movement. On the other hand, issues regarding privacy and use of algorithmic proxies take on increased importance as insurers' extensive use of data and computational abilities evolve.

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“…Social fairness does not necessarily coincide with individual fairness. These two concepts of 'fairness' clash and the conflict is exacerbated, rather than resolved, when the insurance company can rely on behavioural data (Barry, 2019;Frezal and Barry, 2019;Meyers and Van Hoyweghen, 2017).…”

Section: Discrimination and Fairnessmentioning

confidence: 99%

From pool to profile: Social consequences of algorithmic prediction in insurance

Cevolini

Esposito

2020

Big Data & Society

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The use of algorithmic prediction in insurance is regarded as the beginning of a new era, because it promises to personalise insurance policies and premiums on the basis of individual behaviour and level of risk. The core idea is that the price of the policy would no longer refer to the calculated uncertainty of a pool of policyholders, with the consequence that everyone would have to pay only for her real exposure to risk. For insurance, however, uncertainty is not only a problem – shared uncertainty is a resource. The availability of individual risk information could undermine the principle of risk-pooling and risk-spreading on which insurance is based. The article examines this disruptive change first by exploring the possible consequences of the use of predictive algorithms to set insurance premiums. Will it endanger the principle of mutualisation of risks, producing new forms of discrimination and exclusion from coverage? In a second step, we analyse how the relationship between the insurer and the policyholder changes when the customer knows that the company has voluminous, and continuously updated, data about her real behaviour.

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Fairness in Uncertainty: Some Limits and Misinterpretations of Actuarial Fairness

Cited by 26 publications

References 36 publications

Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Machine Learning in P&C Insurance: A Review for Pricing and Reserving

The Discriminating (Pricing) Actuary

From pool to profile: Social consequences of algorithmic prediction in insurance

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