Automotive Consumer Loans Risk Assessment Predictive Modeling Using Generative Adversarial Network and Stacked Autoencoder Neural Networks

Xueping Zhu; Lin Qin; Suzani  Mohamad Samuri; Muhamad Hariz  Muhamad Adnan

doi:10.37934/araset.51.2.4556

Authors

Xueping Zhu Nanning University, Guangxi, 541001, China
Lin Qin Guangxi Academy of Social Sciences, Guangxi, 530022, China
Suzani Mohamad Samuri Faculty of Computing and Meta-Technology, Sultan Idris Education University, 35900 Tanjong Malim, Perak, Malaysia
Muhamad Hariz Muhamad Adnan Faculty of Computing and Meta-Technology, Sultan Idris Education University, 35900 Tanjong Malim, Perak, Malaysia

DOI:

https://doi.org/10.37934/araset.51.2.4556

Keywords:

Generative adversarial networks, stacked autoencoder neural networks, risk assessment, predictive modeling, big data, feature selection, class imbalance

Abstract

In this study, we propose a predictive model for automotive consumer loans risk assessment, leveraging Generative Adversarial Networks (GANs) and Stacked Autoencoder Neural Networks (SAEs). We address issues of high dimensionality and sparsity inherent in big data environments and tackle the class imbalance problem using GANs. Feature selection is effectively carried out using SAEs. Experimental results prove the superiority of our approach over traditional neural networks and our model without GANs or SAEs. The proposed model shows significant potential for application in personal credit risk assessment within automotive finance and beyond. Future work is aimed at extending and improving our model and applying it to other domains.