Default risk prediction and feature extraction using a penalized deep neural network

Cunjie Lin; Nan Qiao; Wenli ZhangYang LiShuangge Ma

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Default risk prediction and feature extraction using a penalized deep neural network

机译：Default risk prediction and feature extraction using a penalized deep neural network

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Abstract Online peer-to-peer lending platforms provide loans directly from lenders to borrowers without passing through traditional financial institutions. For lenders on these platforms to avoid loss, it is crucial that they accurately assess default risk so that they can make appropriate decisions. In this study, we develop a penalized deep learning model to predict default risk based on survival data. As opposed to simply predicting whether default will occur, we focus on predicting the probability of default over time. Moreover, by adding an additional one-to-one layer in the neural network, we achieve feature selection and estimation simultaneously by incorporating an L1documentclass[12pt]{minimal} usepackage{amsmath} usepackage{wasysym} usepackage{amsfonts} usepackage{amssymb} usepackage{amsbsy} usepackage{mathrsfs} usepackage{upgreek} setlength{oddsidemargin}{-69pt} begin{document}$$L_1$$end{document}-penalty into the objective function. The minibatch gradient descent algorithm makes it possible to handle massive data. An analysis of a real-world loan data and simulations demonstrate the model’s competitive practical performance, which suggests favorable potential applications in peer-to-peer lending platforms.

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《Statistics and computing》 |2022年第5期|76.1-76.17|共17页
作者
Cunjie Lin; Nan Qiao; Wenli ZhangYang LiShuangge Ma;
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Renmin University of China||Renmin University of China;

Renmin University of China;

Renmin University of China||Renmin University of China||Renmin University of ChinaRenmin University of China||Yale University;

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正文语种英语
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关键词
Feature extraction; Loan data; Neural network; Risk prediction; Survival analysis;

Default risk prediction and feature extraction using a penalized deep neural network

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