Title: Credit scoring using neural networks and SURE posterior probability calibration

Authors: Matthieu Garcin; Samuel Stéphan

Addresses: Research Center, Léonard de Vinci Pôle Universitaire, 92916 Paris La Défense, France ' Research Center, Léonard de Vinci Pôle Universitaire, 92916 Paris La Défense, France; SAMM, Université Paris 1 Panthéon-Sorbonne, 90 Rue de Tolbiac, 75013 Paris cedex 13, France

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 the performance a little. We also consider different sets of features in order to assess their importance in terms of prediction accuracy. We found that temporal features 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 such as the sigmoid function and 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 results.

Keywords: deep learning; credit scoring; calibration; Stein's unbiased risk estimate; SURE.

DOI: 10.1504/IJADS.2026.151983

International Journal of Applied Decision Sciences, 2026 Vol.19 No.2, pp.155 - 179

Received: 18 Mar 2024
Accepted: 22 Dec 2024
Published online: 02 Mar 2026 *

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