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Another look at the zero integral difference between lorenz and concentration curves in supervised learning

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Listed:
  • Denuit, Michel

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Trufin, Julien

    (ULB)

Abstract

We revisit the area between the concentration and Lorenz curves (ABC) criterion for model assessment in supervised learning. Insurance pricing is considered throughout the paper to illustrate the concepts but the results apply to any other setting where the mean of a response must be estimated from data. Building on the characterization of these curves, we provide new equivalent formulations for the case where the ABC vanishes. First, we characterize a vanishing ABC as the absence of correlation between pricing error and the ranks induced by the candidate premiums, making the link with Gini and Co-Gini coefficients. Inboth the discrete and continuous cases, we then show that a vanishing ABC corresponds toglobal balance in a modified portfolio that overweights lower premium classes. These results complement existing work on auto-calibration and contribute to a better understanding of ABC as a diagnostic tool in insurance pricing and related applications.

Suggested Citation

  • Denuit, Michel & Trufin, Julien, 2025. "Another look at the zero integral difference between lorenz and concentration curves in supervised learning," LIDAM Discussion Papers ISBA 2025026, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2025026
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    References listed on IDEAS

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    1. Fabian Krüger & Johanna F. Ziegel, 2021. "Generic Conditions for Forecast Dominance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 972-983, October.
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