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Improving Classifier Performance Assessment of Credit Scoring Models

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  • Raffaella Calabrese

    (Dynamics Lab, Geary Institute, University College Dublin)

Abstract

In evaluating credit scoring predictive power it is common to use the Re-ceiver Operating Characteristics (ROC) curve, the Area Under the Curve(AUC) and the minimum probability-weighted loss. The main weakness of the rst two assessments is not to take the costs of misclassi cation errors into account and the last one depends on the number of defaults in the credit portfolio. The main purposes of this paper are to provide a curve, called curve of Misclassi cation Error Loss (MEL), and a classi er performance measure that overcome the above-mentioned drawbacks. We prove that the ROC dominance is equivalent to the MEL dominance. Furthermore, we derive the probability distribution of the proposed predictive power measure and we analyse its performance by Monte Carlo simulations. Finally, we apply the suggested methodologies to empirical data on Italian Small and Medium Enterprisers.

Suggested Citation

  • Raffaella Calabrese, 2012. "Improving Classifier Performance Assessment of Credit Scoring Models," Working Papers 201204, Geary Institute, University College Dublin.
  • Handle: RePEc:ucd:wpaper:201204
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    References listed on IDEAS

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    Keywords

    Performance Assessment; Credit Scoring Modules; Monte Carlo simulations; Italian Enterprisers;
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