A novel deep ensemble model for imbalanced credit scoring in internet finance

Most existing deep ensemble credit scoring models have considered deep neural net-works, for which the structures are difficult to design and the modeling results are difficult to interpret. Moreover, the methods of dealing with the class-imbalance problem in these studies are still based on traditional resampling methods. To fill these gaps, we combine a new over-sampling method, the variational autoencoder (VAE), and a deep ensemble classifier, the deep forest (DF), and propose a novel deep ensemble model for credit scoring in internet finance, VAE-DF. We train and test our model using a number of credit scoring datasets in internet finance and find that our model exhibits good performance and can realize a self-adapting depth. The results show that VAE-DF is an effective credit scoring tool, especially for highly class-imbalanced and non-linear datasets in internet finance, due to its strong ability to learn the complex distributions of these datasets.(c) 2023 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.