DPTree and DPForest: tree-based methods fulfilling demographic parity

Pierre-Alexandre Simon, Michel Denuit and Julien Trufin
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Pierre-Alexandre Simon: Université Libre de Bruxelles
Michel Denuit: Université catholique de Louvain, LIDAM/ISBA, Belgium
Julien Trufin: Université Libre de Bruxelles

No 2026009, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: Tree-based methods are widely used in insurance pricing due to their simple and accurate splitting rules. However, there is no guarantee that the resulting premiums avoid indirect discrimination when features recorded in the database are correlated with the protected variable under consideration. This paper shows that splitting rules in regression trees and random forests can be adapted in order to avoid indirect discrimination related to a binary protected variable like gender. The new procedure is illustrated on motor third-party liability insurance claim data.

Keywords: Demographic parity; insurance risk classification; random forests; regression trees (search for similar items in EconPapers)
Pages: 19
Date: 2026-03-31
Note: In: Annals of Actuarial Science, 2026, vol. 20(1), p. 96-114
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2026009

DOI: 10.1017/S1748499525100092

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