TY - GEN
T1 - Overdue prediction of bank loans based on LSTM-SVM
AU - Li, Xin
AU - Long, Xianzhong
AU - Sun, Guozi
AU - Yang, Geng
AU - Li, Huakang
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/4
Y1 - 2018/12/4
N2 - In the aspect of bank loans, the accuracy of traditional user loan risk prediction models, such as KNN, Bayesian, DNN, are not benefit from the data growth. This article is based on the work of Overdue Prediction of Bank Loans Based on Deep Neural Network. And we propose to analyze the dynamic behavior of users by LSTM algorithm, and use the SVM algorithm to analyze the user's static data to solve the current prediction problems. This article uses users' basic information, bank records, user browsing behavior, credit card billing records, and loan time information to evaluate whether users are delinquent. These static data are the basic input for SVM. For LSTM model, we extract user's recent transaction type from browsing behavior as input to LSTM, to predict the probability of users' overdue behavior. Finally, we calculate the average of the two algorithms as the final result. From the experimental results, this LSTM-SVM model shows a great improvement than traditional algorithms.
AB - In the aspect of bank loans, the accuracy of traditional user loan risk prediction models, such as KNN, Bayesian, DNN, are not benefit from the data growth. This article is based on the work of Overdue Prediction of Bank Loans Based on Deep Neural Network. And we propose to analyze the dynamic behavior of users by LSTM algorithm, and use the SVM algorithm to analyze the user's static data to solve the current prediction problems. This article uses users' basic information, bank records, user browsing behavior, credit card billing records, and loan time information to evaluate whether users are delinquent. These static data are the basic input for SVM. For LSTM model, we extract user's recent transaction type from browsing behavior as input to LSTM, to predict the probability of users' overdue behavior. Finally, we calculate the average of the two algorithms as the final result. From the experimental results, this LSTM-SVM model shows a great improvement than traditional algorithms.
KW - Bank Loans
KW - LSTM
KW - Overdue Prediction
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=85060290874&partnerID=8YFLogxK
U2 - 10.1109/SmartWorld.2018.00312
DO - 10.1109/SmartWorld.2018.00312
M3 - Conference Proceeding
AN - SCOPUS:85060290874
T3 - Proceedings - 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
SP - 1859
EP - 1863
BT - Proceedings - 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
A2 - Loulergue, Frederic
A2 - Wang, Guojun
A2 - Bhuiyan, Md Zakirul Alam
A2 - Ma, Xiaoxing
A2 - Li, Peng
A2 - Roveri, Manuel
A2 - Han, Qi
A2 - Chen, Lei
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE SmartWorld, 15th IEEE International Conference on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
Y2 - 7 October 2018 through 11 October 2018
ER -