Olivier Côté

Olivier Côté

Ph.D. Candidate in Actuarial Science, ACAS

Publications

You can also find my articles on my Google Scholar profile.

A Statistical Perspective on Fairness in Insurance Pricing

Olivier Côté, Marie-Pier Côté, and Arthur Charpentier

In press at Annual Review of Statistics and Its Application, 2027

A guided tour of unfairness mitigation in insurance pricing, from autocalibration to discrimination-free pricing and optimal transport, and of the statistical challenges in implementing antidiscrimination regulation.

Talks (4)
CAS (webinar), 2026ARC (AAA invited session), 2026Association des actuaires en IARD du Québec (AAIARD), 2026 Industry: Promutuel Assurance
BibTeX
@article{Cote/etal:2027statistical,
author = {Côté, Olivier and Côté, Marie-Pier and Charpentier, Arthur},
title = {A statistical perspective on fairness in insurance pricing},
journal = {Annual Review of Statistics and Its Application},
year = {2027},
note = {In press}
}

Governing Fairness in Insurance Pricing: The Trilemma of Actuarial Adequacy, Solidarity, and Causal Legitimacy

Arthur Charpentier, Olivier Côté, and Marie-Pier Côté

Published in Journal of Business Ethics, 2026

Every premium is a normative choice: price fairness is governed by a trilemma between actuarial adequacy, solidarity, and causal legitimacy, and we build the governance architecture (scorecard, causal due diligence, comply-or-explain oversight) to manage it as such.

Talks (4)
CAS (webinar), 2026ARC (AAA invited session), 2026Association des actuaires en IARD du Québec (AAIARD), 2026 Industry: Promutuel Assurance
BibTeX
@article{Charpentier/etal:2026governance,
author = {Charpentier, Arthur and Côté, Olivier and Côté, Marie-Pier},
title = {Governing fairness in insurance pricing: the trilemma of actuarial adequacy, solidarity, and causal legitimacy},
journal = {Journal of Business Ethics},
year = {2026},
doi = {10.1007/s10551-026-06479-2},
url = {https://link.springer.com/article/10.1007/s10551-026-06479-2}
}

Recoverability of Market-Wide Fair Insurance Premiums under Selection Bias

Marie-Pier Côté, Olivier Côté, and Arthur Charpentier

Published in Insurance: Mathematics and Economics, 2026

When and how market-wide fair premiums can be recovered from a single insurer’s portfolio, despite selection bias.

Talks (3)
Department of Mathematics, Stockholm University, 2026ARC (CAS invited session), 2025Institut intelligence et données, 2024
BibTeX
@article{Cote/etal:2026recoverability,
author = {Côté, Marie-Pier and Côté, Olivier and Charpentier, Arthur},
title = {Recoverability of market-wide fair insurance premiums under selection bias},
journal = {Insurance: Mathematics and Economics},
volume = {129},
pages = {103268},
year = {2026},
doi = {10.1016/j.insmatheco.2026.103268},
url = {https://www.sciencedirect.com/science/article/pii/S0167668726000582}
}

A Scalable Toolbox for Exposing Indirect Discrimination in Insurance Rates

Olivier Côté, Marie-Pier Côté, and Arthur Charpentier

Published in Casualty Actuarial Society Research Paper Series on Bias and Insurance, 2026

Winner, AAA 2026 Award for Research Online supplement

Proxy effects and unfair biases in insurance rates, quantified in the terms actuaries need: dollars and people, along the three core fairness dimensions of actuarial fairness, solidarity, and causality. See the CAS announcement.

Talks (16)
CAS (webinar), 2026ARC (AAA invited session), 2026Association des actuaires en IARD du Québec (AAIARD), 2026Department of Mathematics, Stockholm University, 20261st ASTIN Bulletin Conference, ETH Zürich, 2026CAS Canada Connect Day, 2025Department of Statistics, University of Toronto, 2025CAS Annual Meeting, 2025ARC (CAS invited session), 2025Université du Québec à Montréal, 2025Perspectives on Actuarial Risks in Talks of Young Researchers (PARTY), 2025Chaire ACTIONS, 2025Milliman R&D, 2025 Industry: Desjardins Groupe d'assurances générales, TD Insurance, Promutuel Assurance
BibTeX
@techreport{Cote/etal:2025toolbox,
author = {Côté, Olivier and Côté, Marie-Pier and Charpentier, Arthur},
title = {A scalable toolbox for exposing indirect discrimination in insurance rates},
institution = {Casualty Actuarial Society},
type = {Research Paper Series on Bias and Insurance},
year = {2026},
url = {https://www.casact.org/sites/default/files/2026-03/Scalable_Toolbox_for_Exposing_Indirect_Discrimination_Research_Paper.pdf}
}

Federated Measurement of Demographic Disparities from Quantile Sketches

Arthur Charpentier, Agathe Fernandes Machado, Olivier Côté, and François Hu

Available at arXiv (under review), 2026

One-shot auditing of demographic parity across data silos, without sharing raw data.

BibTeX
@misc{Charpentier/etal:2026federated,
author = {Charpentier, Arthur and Fernandes Machado, Agathe and Côté, Olivier and Hu, François},
title = {Federated measurement of demographic disparities from quantile sketches},
year = {2026},
eprint = {2602.18870},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2602.18870}
}

A Fair Price to Pay: Exploiting Causal Graphs for Fairness in Insurance

Olivier Côté, Marie-Pier Côté, and Arthur Charpentier

Published in Journal of Risk and Insurance, 2025

Finalist, AAA 2025 Award for Research

A causal graph tailored for insurance gives formal definitions of direct and indirect discrimination, and sorts fair pricing methodologies into five families by the fairness properties they can deliver.

Talks (14)
CAS (webinar), 2026AAA (webinar), 2026Department of Mathematics, Stockholm University, 2026ARC (AAA invited panel), 2025ARC (CAS invited session), 2025Chaire ACTIONS, 2025Milliman R&D, 2025Institut intelligence et données, 2024IMEC, 2024Workshop on fairness and discrimination in insurance, 2024Autorité des marchés financiers, 2024Insurance Data Science Conference, 2023 Industry: Intact Financial Corporation, Desjardins Groupe d'assurances générales
BibTeX
@article{Cote/etal:2024,
author = {Côté, Olivier and Côté, Marie-Pier and Charpentier, Arthur},
title = {A fair price to pay: Exploiting causal graphs for fairness in insurance},
journal = {Journal of Risk and Insurance},
volume = {92},
pages = {33-75},
year = {2025},
keywords = {bias, causal inference, directed acyclic graph, discrimination, disparate impact, fairness criteria, score},
doi = {10.1111/jori.12503},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/jori.12503},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/jori.12503}
}