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 paper 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 mutualization 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.

From Pool to Profile: Social Consequences of Algorithmic Prediction in Insurance / Cevolini, Alberto; Esposito, Elena. - In: BIG DATA & SOCIETY. - ISSN 2053-9517. - 7:2(2020), pp. 1-11. [10.1177/2053951720939228]

From Pool to Profile: Social Consequences of Algorithmic Prediction in Insurance

Alberto Cevolini
;
Elena Esposito
2020

Abstract

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 paper 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 mutualization 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.
2020
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From Pool to Profile: Social Consequences of Algorithmic Prediction in Insurance / Cevolini, Alberto; Esposito, Elena. - In: BIG DATA & SOCIETY. - ISSN 2053-9517. - 7:2(2020), pp. 1-11. [10.1177/2053951720939228]
Cevolini, Alberto; Esposito, Elena
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1207456
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