Credit scoring using neural networks and SURE posterior probability calibration - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2023

Credit scoring using neural networks and SURE posterior probability calibration

Résumé

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.
Fichier principal
Vignette du fichier
Article_1.pdf (490.62 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03286760 , version 1 (15-07-2021)
hal-03286760 , version 2 (20-10-2023)

Identifiants

  • HAL Id : hal-03286760 , version 2

Citer

Matthieu Garcin, Samuel Stéphan. Credit scoring using neural networks and SURE posterior probability calibration. 2023. ⟨hal-03286760v2⟩
91 Consultations
272 Téléchargements

Partager

Gmail Facebook X LinkedIn More