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The total cost of misclassification in credit scoring: A comparison of generalized linear models and generalized additive models

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  • Christian Lohmann
  • Thorsten Ohliger

Abstract

This study examines whether the evaluation of a bankruptcy prediction model should take into account the total cost of misclassification. For this purpose, we introduce and apply a validity measure in credit scoring that is based on the total cost of misclassification. Specifically, we use comprehensive data from the annual financial statements of a sample of German companies and analyze the total cost of misclassification by comparing a generalized linear model and a generalized additive model with regard to their ability to predict a company's probability of default. On the basis of these data, the validity measure we introduce shows that, compared to generalized linear models, generalized additive models can reduce substantially the extent of misclassification and the total cost that this entails. The validity measure we introduce is informative and justifies the argument that generalized additive models should be preferred, although such models are more complex than generalized linear models. We conclude that to balance a model's validity and complexity, it is necessary to take into account the total cost of misclassification.

Suggested Citation

  • Christian Lohmann & Thorsten Ohliger, 2019. "The total cost of misclassification in credit scoring: A comparison of generalized linear models and generalized additive models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(5), pages 375-389, August.
  • Handle: RePEc:wly:jforec:v:38:y:2019:i:5:p:375-389
    DOI: 10.1002/for.2545
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    Cited by:

    1. Yufei Xia & Xinyi Guo & Yinguo Li & Lingyun He & Xueyuan Chen, 2022. "Deep learning meets decision trees: An application of a heterogeneous deep forest approach in credit scoring for online consumer lending," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1669-1690, December.
    2. Maria Tragouda & Michalis Doumpos & Constantin Zopounidis, 2024. "Identification of fraudulent financial statements through a multiā€label classification approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.

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