A Multi-Criteria Fair Gaussian Regressor for Insurance Premium

Charlotte Jamotton () and Donatien Hainaut ()
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Charlotte Jamotton: Université catholique de Louvain, LIDAM/ISBA, Belgium
Donatien Hainaut: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2026003, LIDAM Discussion Papers ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: This article studies how multiple notions of fairness can be incorporated into a single Bayesian non-parametric regression framework for insurance pricing, witha focus on claim frequency modeling under a log-link. We consider a Generalized Gaussian Process Regression (GGPR) model for count data with risk exposure and introduce fairness interventions in its architecture. Specifically, we addressnotions of individual fairness by altering the kernel structure to control the similarity between policies (e.g., to mitigate omitted variable bias). We also address group-level fairness by enforcing demographic parity through linear constraints affecting the posterior. This modified GGPR architecture allows us to jointly enforce multiple fairness definitions, spanning both group and individual-level criteria, within a single probabilistic model. We empirically explore trade-offs with actuarial fairness, and how different fairness criteria interact when combined. The results highlight the importance of adopting a multi-criteria, context-aware approach to fairness in insurance pricing.

Keywords: Gaussian process regression; count data; confounding bias; actuarial fairness; omitted variable bias; indirect discrimination; direct discrimination (search for similar items in EconPapers)
Pages: 53
Date: 2026-02-23
New Economics Papers: this item is included in nep-ecm
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