Affiliation:
1. Joint Financial Technology Lab Chery HuiYin Motor Finance Service Company Ltd. Wuhu China
2. Big Data Governance and Innovation Lab Yangtze River Delta Information Intelligence Innovation Research Institute Wuhu China
Abstract
AbstractIn 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.
Subject
General Engineering,General Computer Science
Reference50 articles.
1. Financing format selection for electronic business platforms with a capital-constrained e-tailer
2. Credit investigation center of the People's Bank of China and institute of finance, internet credit, credit risk management and credit investigation;Ji ZH;Financ Res,2014
3. Developing prediction model of loan risk in banks using data mining;Hamid AJ;Mach Learn Appl Int J,2016
4. Research on financial customer credit evaluation based on XGBoost algorithm model;Jianjian L;Softw Guide,2019
5. Personal credit assessment based on improved GS‐XGBoost;Xin L;Comput Syst Appl,2020
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