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Credit scoring using neural networks and SURE posterior probability calibration

Author

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  • Matthieu Garcin

    (ESILV - École Supérieure d'Ingénierie Léonard de Vinci)

  • Samuel Stéphan

    (ESILV - École Supérieure d'Ingénierie Léonard de Vinci, SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) - UP1 - Université Paris 1 Panthéon-Sorbonne)

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 find 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 the SURE calibration technique is able to calibrate the predicted probabilities as well as the classical Platt method.

Suggested Citation

  • Matthieu Garcin & Samuel Stéphan, 2023. "Credit scoring using neural networks and SURE posterior probability calibration," Working Papers hal-03286760, HAL.
    • Handle: RePEc:hal:wpaper:hal-03286760
    Note: View the original document on HAL open archive server: https://hal.science/hal-03286760v2
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    References listed on IDEAS

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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.
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    Keywords

    Deep learning; credit scoring; calibration; SURE;
    All these keywords.

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