Model selection based on Lorenz and concentration curves, Gini indices and convex order

In order to determine an appropriate amount of premium, statistical goodness-of-fit criteria must be supplemented with actuarial ones when assessing performance of a given candidate pure premium. In this paper, concentration curves and Lorenz curves are shown to provide actuaries with effective tools to evaluate whether a premium is appropriate or to compare two competing alternatives. The idea is to compare the premium income for sub-portfolios gathering low risks (identified as low by means of the premiums under consideration) to the true one, or equivalently, to the actual losses. Numerical illustrations performed on hypothetical data and real ones demonstrate the usefulness of the proposed approach.

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Document type	Article de périodique (Journal article) – Article de recherche
Access type	Accès libre
Publication date	2019
Language	Anglais
Journal information	"Insurance: Mathematics and Economics" - Vol. 89, p. 128-139 (2019)
Peer reviewed	yes
Publisher	Elsevier BV
issn	0167-6687
Publication status	Publié
Affiliation	UCL - SSH/LIDAM/ISBA - Institut de Statistique, Biostatistique et Sciences Actuarielles
Keywords	Pricing ; Risk classification ; Concentration curve ; Lorenz curve ; GLM ; GBM ; Trees ; Neural networks
Links	https://doi.org/10.1016/j.insmatheco.2019.09.001[DOI] http://hdl.handle.net/2078.1/220948[Handle]

See also
	[boreal:214859]	Model selection based on Lorenz and concentration curves, Gini indices and convex order