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Classification in automobile insurance using a DEA and discriminant analysis hybrid

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Abstract

In this article, we adopt an efficiency approach to the two-group linear programming method of discriminant analysis (DA), using principles taken from data envelopment analysis (DEA), to predict group membership in an insurance underwriting scheme. Using an empirical insurance data base we illustrate the effectiveness of our model as a decision-making tool to distinguish among automobile insurance applicants by contrasting our hybrid model with both statistical and LP methods of discriminant analysis. We find for this insurance application that our hybrid model significantly outperforms the more traditional methods in separation and misclassification outcomes.

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Authors and Affiliations

  1. MIS/DS Department, University of Memphis, 38152, Memphis, TN

    Donna Retzlaff-Roberts

  2. Edwin L. Cox School of Business, Southern Methodist University, 75275, Dallas, TX

    Robert Puelz

Authors
  1. Donna Retzlaff-Roberts
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  2. Robert Puelz
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Retzlaff-Roberts, D., Puelz, R. Classification in automobile insurance using a DEA and discriminant analysis hybrid. J Prod Anal 7, 417–427 (1996). https://doi.org/10.1007/BF00162050

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  • DOI: https://doi.org/10.1007/BF00162050

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