Insurance, Big Data and Changing Conceptions of Fairness

Barry, Laurence

doi:10.1017/s0003975620000089

Cited by 22 publications

(9 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…The history of insurance and premium calculation in the Western world is a surprisingly rich and fascinating one. Historically, harmful events were not understood as chance occurrences; rather, "accidents and diseases were seen as a punishment for objectionable deeds, known only to God and the sinner" [5]. Spreading the burdens of these misfortunes across a larger group of people made no sense under this worldview, as someone's chance of experiencing a misfortune was thought to be entirely under their control [6].…”

Section: Debates Over Fairness In Insurance: Solidarity and Actuarial...mentioning

confidence: 99%

“…Spreading the burdens of these misfortunes across a larger group of people made no sense under this worldview, as someone's chance of experiencing a misfortune was thought to be entirely under their control [6]. It was only during the Industrial Revolution that these views started to change, as workplace accidents began to be seen as not necessarily the fault of workers or managers, but as "inherent to industrialization itself," leading some to conclude "that the old rule of responsibility was obsolete" [5].…”

Section: Debates Over Fairness In Insurance: Solidarity and Actuarial...mentioning

confidence: 99%

See 1 more Smart Citation

Better Together? How Externalities of Size Complicate Notions of Solidarity and Actuarial Fairness

Donahue¹,

Barocas²

2021

Preprint

View full text Add to dashboard Cite

Consider a cost-sharing game with players of different contribution to the total cost: an example might be an insurance company calculating premiums for a population of mixed-risk individuals. Two natural and competing notions of fairness might be to a) charge each individual the same price or b) charge each individual according to the cost that they bring to the pool. In the insurance literature, these general approaches are referred to as solidarity and actuarial fairness and are commonly viewed as opposites. However, in insurance (and many other natural settings), the cost-sharing game also exhibits externalities of size: all else being equal, larger groups have lower average cost. In the insurance case, we analyze a model with externalities of size due to a reduction in the variability of losses. We explore how this complicates traditional understandings of fairness, drawing on literature in cooperative game theory.First, we explore solidarity: we show that it is possible for both groups (high and low risk) to strictly benefit by joining an insurance pool where costs are evenly split, as opposed to being in separate risk pools. We build on this by producing a pricing scheme that maximally subsidizes the high risk group, while maintaining an incentive for lower risk people to stay in the insurance pool. Next, we demonstrate that with this new model, the price charged to each individual has to depend on the risk of other participants, making naive actuarial fairness inefficient. Furthermore, we prove that stable pricing schemes must be ones where players have the anti-social incentive of desiring riskier partners, contradicting motivations for using actuarial fairness. Finally, we describe how these results relate to debates about fairness in machine learning and potential avenues for future research.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Insurance, Big Data and Changing Conceptions of Fairness

Cited by 22 publications

References 37 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

Better Together? How Externalities of Size Complicate Notions of Solidarity and Actuarial Fairness

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

Contact Info

Product

Resources

About