Joint loan risk prediction based on deep learning‐optimized stacking model

Abstract

In recent years, China's automobile industry has undergone rapid development, creating new opportunities for the auto loan industry. Currently, auto financing companies are actively seeking to expand their cooperation with banks. Therefore, improving the approval rate and scale of joint loan business is of significant practical importance. In this paper, we propose a Stacking‐based financial institution risk approval model and select the optimal stacking model by comparing its performance with other models. Additionally, we construct a bank approval model using deep learning techniques on a biased data set, with feature extraction performed using convolution neural networks (CNN) and feature‐based counterfactual augmentation used for balanced sampling. Finally, we optimize the model of the prediction of auto finance companies by selecting the optimal coefficients of loss function based on the features and results of the bank approval model. The proposed approach leads to an approximately 6% increase in the joint loan approval rate on the actual data set, as demonstrated by experimental results.

Machine Learning for an Enhanced Credit Risk Analysis: A Comparative Study of Loan Approval Prediction Models Integrating Mental Health Data

“…Wang and colleagues (2023) [9] devised a novel stacking-based model aimed at evaluating the risks in financial institutions, determining the most effective model through performance comparisons. Their work extended to crafting a bank approval model using deep learning on imbalanced data, employing a convolutional neural network for feature extraction, and implementing counterfactual augmentation for achieving balanced sampling results.…”