Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm. (26th September 2020)

• Record Type: Journal Article
• Authors: Niu, Aiwen; Cai, Bingqing; Cai, Shousong
• Other Names: Lv Zhihan Guest Editor.
• Is Part Of: Complexity. Volume 2020(2020)
• Publication Date: 2020-09-26
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2020/8563030
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : With the continuous development of big data technology, the data of online lending platform witness explosive development. How to give full play to the advantages of data, establish a credit risk assessment model, and realize the effective control of platform credit risk have become the focus of online lending platform. In view of the fact that the network loan data are mainly unbalanced data, the smote algorithm is helpful to optimize the model and improve the evaluation performance of the model. Relevant research shows that stochastic forest model has higher applicability in credit risk assessment, and cart, ANN, C4.5, and other algorithms are also widely used. In the influencing factors of credit evaluation, the weight of the applicant's enterprise scale, working years, historical records, credit score, and other indicators is relatively high, while the index weight of marriage and housing/car production (loan) is relatively low.