A Five-Phase Framework for Fair Insurance: Reviewing Strategies for Digital Price Differentiation

Rijk Mercuur, Sieuwert van Otterloo, Huib Aldewereld
Proceedings of Fourth European Workshop on Algorithmic Fairness, PMLR 294:251-264, 2025.

Abstract

Insurers increasingly use machine learning to assess financial risk and determine personalized premiums with the aim of ensuring income and financial stability. Despite the recent focus on fairness, insurance companies and software developers struggle to bridge the gap between fairness principles and practical implementation. Justifying digital price differentiation in terms of both fairness and profit is a socio-technical problem: it requires an integration of organizational processes, ethical-legal considerations on indirect discrimination, and technical fairness metrics and mitigation techniques. The paper proposes a structured list of 33 strategies designed to help organizations navigate these challenges, derived from a survey of Dutch insurance professionals, a systematic review of academic literature, and expert evaluations. The strategies are organized into five phases: Understand, Determine, Adjust, Evaluate and Communicate, with a particular emphasis on aligning fairness principles with actuarial accuracy and compliance with legal standards. This work contributes to the literature by offering an overview of actionable strategies that go beyond fairness metrics, addressing both technical and social aspects of digital price differentiation. Practically, the strategy list supports insurance professionals — including data scientists, actuaries, auditors, compliance officers, and communication staff — by (1) providing a comprehensive overview of strategies to balance fairness and profitability in digital price differentiation, and (2) offering a framework to structure organizational processes and internal communication around this balance.

Cite this Paper

BibTeX
@InProceedings{pmlr-v294-mercuur25a, title = {A Five-Phase Framework for Fair Insurance: Reviewing Strategies for Digital Price Differentiation}, author = {Mercuur, Rijk and van Otterloo, Sieuwert and Aldewereld, Huib}, booktitle = {Proceedings of Fourth European Workshop on Algorithmic Fairness}, pages = {251--264}, year = {2025}, editor = {Weerts, Hilde and Pechenizkiy, Mykola and Allhutter, Doris and Corrêa, Ana Maria and Grote, Thomas and Liem, Cynthia}, volume = {294}, series = {Proceedings of Machine Learning Research}, month = {30 Jun--02 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v294/main/assets/mercuur25a/mercuur25a.pdf}, url = {https://proceedings.mlr.press/v294/mercuur25a.html}, abstract = {Insurers increasingly use machine learning to assess financial risk and determine personalized premiums with the aim of ensuring income and financial stability. Despite the recent focus on fairness, insurance companies and software developers struggle to bridge the gap between fairness principles and practical implementation. Justifying digital price differentiation in terms of both fairness and profit is a socio-technical problem: it requires an integration of organizational processes, ethical-legal considerations on indirect discrimination, and technical fairness metrics and mitigation techniques. The paper proposes a structured list of 33 strategies designed to help organizations navigate these challenges, derived from a survey of Dutch insurance professionals, a systematic review of academic literature, and expert evaluations. The strategies are organized into five phases: Understand, Determine, Adjust, Evaluate and Communicate, with a particular emphasis on aligning fairness principles with actuarial accuracy and compliance with legal standards. This work contributes to the literature by offering an overview of actionable strategies that go beyond fairness metrics, addressing both technical and social aspects of digital price differentiation. Practically, the strategy list supports insurance professionals — including data scientists, actuaries, auditors, compliance officers, and communication staff — by (1) providing a comprehensive overview of strategies to balance fairness and profitability in digital price differentiation, and (2) offering a framework to structure organizational processes and internal communication around this balance.} }
Endnote
%0 Conference Paper %T A Five-Phase Framework for Fair Insurance: Reviewing Strategies for Digital Price Differentiation %A Rijk Mercuur %A Sieuwert van Otterloo %A Huib Aldewereld %B Proceedings of Fourth European Workshop on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2025 %E Hilde Weerts %E Mykola Pechenizkiy %E Doris Allhutter %E Ana Maria Corrêa %E Thomas Grote %E Cynthia Liem %F pmlr-v294-mercuur25a %I PMLR %P 251--264 %U https://proceedings.mlr.press/v294/mercuur25a.html %V 294 %X Insurers increasingly use machine learning to assess financial risk and determine personalized premiums with the aim of ensuring income and financial stability. Despite the recent focus on fairness, insurance companies and software developers struggle to bridge the gap between fairness principles and practical implementation. Justifying digital price differentiation in terms of both fairness and profit is a socio-technical problem: it requires an integration of organizational processes, ethical-legal considerations on indirect discrimination, and technical fairness metrics and mitigation techniques. The paper proposes a structured list of 33 strategies designed to help organizations navigate these challenges, derived from a survey of Dutch insurance professionals, a systematic review of academic literature, and expert evaluations. The strategies are organized into five phases: Understand, Determine, Adjust, Evaluate and Communicate, with a particular emphasis on aligning fairness principles with actuarial accuracy and compliance with legal standards. This work contributes to the literature by offering an overview of actionable strategies that go beyond fairness metrics, addressing both technical and social aspects of digital price differentiation. Practically, the strategy list supports insurance professionals — including data scientists, actuaries, auditors, compliance officers, and communication staff — by (1) providing a comprehensive overview of strategies to balance fairness and profitability in digital price differentiation, and (2) offering a framework to structure organizational processes and internal communication around this balance.
APA
Mercuur, R., van Otterloo, S. & Aldewereld, H.. (2025). A Five-Phase Framework for Fair Insurance: Reviewing Strategies for Digital Price Differentiation. Proceedings of Fourth European Workshop on Algorithmic Fairness, in Proceedings of Machine Learning Research 294:251-264 Available from https://proceedings.mlr.press/v294/mercuur25a.html.

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