Cited by 4Insurance and Financial Risk ManagementConsumer Market Behavior and PricingEthics and Social Impacts of AI

Welfare Implications of Fairness and Accountability for Insurance Pricing

Open access
Shimao, Hajime, Huang, Fei·2022·SSRN Electronic Journal

Integrity signals

Registry

Crossref · 10.2139/ssrn.4225159

Cites

24 of 24 references verified0 citation errors · 0 not checkable

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  • In practice, the pure premium for policyholders with the same risk profile may deviate significantly, especially when price optimization is allowed.

    “However, in practice, the pure premium for policyholders with the same risk profile may deviate significantly, especially when price optimization is allowed; see Shimao and Huang (2022) for further discussion.”

    From Antidiscrimination Insurance Pricing: Regulations, Fairness Criteria, and Models (2024) · cites this paper as Shimao and Huang (2022)

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Authors
Shimao, H., & Huang, F.
Affiliations
  • Santa Fe Institute (US)
  • UNSW Sydney (AU)
Corresponding author
Huang, Fei
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2022
ISSN
1556-5068 (ISSN-L)
OpenAlex
W4312878395
Cited by
4
References
23
Citations by year
2026: 1 · 2024: 2 · 2023: 1
Type
posted content
Language
en
Open access
Locations
SSRN Electronic Journal (publishedVersion)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Insurance and Financial Risk Management
  • Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
insurance pricing, price regulation, fairness, accountability, welfare analysis, demographic parity, automobile insurance, compulsory insurance, voluntary insurance, market competition, monopoly, consumer welfare, firm profitability

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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