Learning P2P Lending Credit Evaluation Bayesian Network from Missing Data

doi:10.23940/ijpe.19.06.p10.15911599

Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (6): 1591-1599.doi: 10.23940/ijpe.19.06.p10.15911599

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Learning P2P Lending Credit Evaluation Bayesian Network from Missing Data

Yali Lv^a,b,*, Jianai Wu^a, Junzhong Miao^a, Weixin Hu^a, and Tong Jing^a

a School of Information Management, Shanxi University of Finance and Economics, Taiyuan, 030006, China
b Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006, China

Submitted on ;
Contact: * E-mail address: sxlvyali@126.com
About author:Yali Lv is an associate professor at Shanxi University of Finance & Economics of China. She received her Ph.D. from Tianjin University. Her research interests include probabilistic reasoning, concept learning, data mining, and machine learning;Jianai Wu is a master's candidate at Shanxi University of Finance & Economics of China. Her research interests include data mining and financial data analysis;Junzhong Miao is a master's candidate at Shanxi University of Finance & Economics of China. His research interests include Bayesian machine learning and concept learning;Weixin Hu is a master's candidate at Shanxi University of Finance & Economics of China. Her research interests include data mining and machine learning;Tong Jing is a master's candidate at Shanxi University of Finance & Economics of China. His research interests include Bayesian machine learning and concept learning.
Supported by:
This work has been funded by the National Natural Science Foundation of China (Nos. 61432011, U1435212, 61322211, and 61672332), the Natural Science Foundation of Shanxi Province, China (Nos. 201801D121115, 2013011016-4), and the Postdoctoral Science Foundation of China (No. 2016M591409).

Abstract

Abstract: Credit evaluation is an important issue for investors in the financial field. However, there is a large amount of missing data in the P2P lending platform. To evaluate borrowers' credit from missing data, a credit evaluation Bayesian network model learning algorithm is proposed based on domain knowledge. Specifically, we first give a credit evaluation Bayesian network (CEBN) model to represent the borrowers' attributions and the relationships between attributions, and then we design the CEBN learning algorithm based on domain knowledge. Furthermore, we analyze and discuss the time complexity of the algorithm. Finally, the experimental results demonstrate that the CEBN model has good interpretability, learning performance, and evaluation performance by comparing it with other methods.

Key words: probabilistic inference, credit evaluation, Bayesian networks, domain knowledge, qualitative influences, P2P lending

Yali Lv, Jianai Wu, Junzhong Miao, Weixin Hu, and Tong Jing. Learning P2P Lending Credit Evaluation Bayesian Network from Missing Data [J]. Int J Performability Eng, 2019, 15(6): 1591-1599.

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Learning P2P Lending Credit Evaluation Bayesian Network from Missing Data

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