Loan Default Prediction Based on Convolutional Neural Network and LightGBM

Qiliang Zhu, Wenhao Ding, Mingsen Xiang, Mengzhen Hu and Ning Zhang
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Qiliang Zhu: North China University of Water Resources and Electric Power, China
Wenhao Ding: North China University of Water Resources and Electric Power, China
Mingsen Xiang: North China University of Water Resources and Electric Power, China
Mengzhen Hu: North China University of Water Resources and Electric Power, China
Ning Zhang: North China University of Water Resources and Electric Power, China

International Journal of Data Warehousing and Mining (IJDWM), 2023, vol. 19, issue 1, 1-16

Abstract: With the change of people's consumption mode, credit consumption has gradually become a new consumption trend. Frequent loan defaults give default prediction more and more attention. This paper proposes a new comprehensive prediction method of loan default. This method combines convolutional neural network and LightGBM algorithm to establish a prediction model. Firstly, the excellent feature extraction ability of convolutional neural network is used to extract features from the original loan data and generate a new feature matrix. Secondly, the new feature matrix is used as input data, and the parameters of LightGBM algorithm are adjusted through grid search so as to build the LightGBM model. Finally, the LightGBM model is trained based on the new feature matrix, and the CNN-LightGBM loan default prediction model is obtained. To verify the effectiveness and superiority of our model, a series of experiments were conducted to compare the proposed prediction model with four classical models. The results show that CNN-LightGBM model is superior to other models in all evaluation indexes.

Date: 2023
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