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Credit scoring using neural networks and SURE posterior probability calibration

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  • Matthieu Garcin
  • Samuel St'ephan

Abstract

In this article we compare the performances of a logistic regression and a feed forward neural network for credit scoring purposes. Our results show that the logistic regression gives quite good results on the dataset and the neural network can improve a little the performance. We also consider different sets of features in order to assess their importance in terms of prediction accuracy. We found that temporal features (i.e. repeated measures over time) can be an important source of information resulting in an increase in the overall model accuracy. Finally, we introduce a new technique for the calibration of predicted probabilities based on Stein's unbiased risk estimate (SURE). This calibration technique can be applied to very general calibration functions. In particular, we detail this method for the sigmoid function as well as for the Kumaraswamy function, which includes the identity as a particular case. We show that stacking the SURE calibration technique with the classical Platt method can improve the calibration of predicted probabilities.

Suggested Citation

  • Matthieu Garcin & Samuel St'ephan, 2021. "Credit scoring using neural networks and SURE posterior probability calibration," Papers 2107.07206, arXiv.org.
    • Handle: RePEc:arx:papers:2107.07206
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    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. Anderson, Raymond, 2007. "The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation," OUP Catalogue, Oxford University Press, number 9780199226405.
    3. Steenackers, A. & Goovaerts, M. J., 1989. "A credit scoring model for personal loans," Insurance: Mathematics and Economics, Elsevier, vol. 8(1), pages 31-34, March.
    4. Artem Bequé & Kristof Coussement & Ross Gayler & Stefan Lessmann, 2017. "Approaches for credit scorecard calibration: An empirical analysis," Post-Print hal-01745262, HAL.
    5. Finlay, Steven, 2011. "Multiple classifier architectures and their application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 210(2), pages 368-378, April.
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