Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm
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 onl...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8563030 |
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| _version_ | 1832560032822067200 |
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| author | Aiwen Niu Bingqing Cai Shousong Cai |
| author_facet | Aiwen Niu Bingqing Cai Shousong Cai |
| author_sort | Aiwen Niu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5472d3f64acf434a97524d8cf8772de1 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-5472d3f64acf434a97524d8cf8772de12025-02-03T01:28:32ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/85630308563030Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE AlgorithmAiwen Niu0Bingqing Cai1Shousong Cai2Glorious Sun School of Business Management, Donghua University, Shanghai 200051, ChinaSchool of Humanities, Shanghai University of Finance and Economics, Shanghai 200433, ChinaSchool of Business Administration, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, ChinaWith 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.http://dx.doi.org/10.1155/2020/8563030 |
| spellingShingle | Aiwen Niu Bingqing Cai Shousong Cai Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm Complexity |
| title | Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm |
| title_full | Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm |
| title_fullStr | Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm |
| title_full_unstemmed | Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm |
| title_short | Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm |
| title_sort | big data analytics for complex credit risk assessment of network lending based on smote algorithm |
| url | http://dx.doi.org/10.1155/2020/8563030 |
| work_keys_str_mv | AT aiwenniu bigdataanalyticsforcomplexcreditriskassessmentofnetworklendingbasedonsmotealgorithm AT bingqingcai bigdataanalyticsforcomplexcreditriskassessmentofnetworklendingbasedonsmotealgorithm AT shousongcai bigdataanalyticsforcomplexcreditriskassessmentofnetworklendingbasedonsmotealgorithm |