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Predicting repayment of borrows in peer‐to‐peer social lending with deep dense convolutional network
Author(s) -
Kim JiYoon,
Cho SungBae
Publication year - 2019
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12403
Subject(s) - computer science , loan , discriminative model , convolutional neural network , feature (linguistics) , database transaction , peer to peer , representation (politics) , deep learning , margin (machine learning) , artificial intelligence , variety (cybernetics) , social network (sociolinguistics) , machine learning , data mining , finance , social media , world wide web , database , linguistics , philosophy , politics , political science , law , economics
In peer‐to‐peer lending, it is important to predict the repayment of the borrower to reduce the lender's financial loss. However, it is difficult to design a powerful feature extractor for predicting the repayment as user and transaction data continue to increase. Convolutional neural networks automatically extract useful features from big data, but they use only high‐level features; hence, it is difficult to capture a variety of representations. In this study, we propose a deep dense convolutional network for repayment prediction in social lending, which maintains the borrower's semantic information and obtains a good representation by automatically extracting important low‐ and high‐level features simultaneously. We predict the repayment of the borrower by learning discriminative features depending on the loan status. Experimental results on the Lending Club dataset show that our model is more effective than other methods. A fivefold cross‐validation is performed to run the experiments.