Automatically Detecting Peer-to-Peer Lending Intermediary Risk—Top Management Team Profile Textual Features Perspective
Peer-to-Peer lending is developing quickly around the world as a new E-finance industry, especially in China. Yet fraudulence and business ceasing of Peer-to-Peer Lending Intermediaries (P2P-INTs) occur frequently, making P2P investors facing serious risk. This paper attempts to explore a bridge con...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8725485/ |
| _version_ | 1818737306864975872 |
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| author | Lei Li Yanjie Feng Yue Lv Xiaoyue Cong Xiangling Fu Jiayin Qi |
| author_facet | Lei Li Yanjie Feng Yue Lv Xiaoyue Cong Xiangling Fu Jiayin Qi |
| author_sort | Lei Li |
| collection | DOAJ |
| description | Peer-to-Peer lending is developing quickly around the world as a new E-finance industry, especially in China. Yet fraudulence and business ceasing of Peer-to-Peer Lending Intermediaries (P2P-INTs) occur frequently, making P2P investors facing serious risk. This paper attempts to explore a bridge connecting managerial research with some most advanced natural language processing (NLP) technologies, and examines the risk assessing power of automatic learning text classifiers based on data of hazard status and top management team profile texts of the P2P-INTs. A risk evaluation model named MULTIPLE NLP Integrated Learning Text Classifier (MUN-LETCLA) based on five NLP techniques and meta-learning is proposed. Then risk classification power of the MUN-LETCLA and the single NLP models is assessed. The results show that the proposed model is effective in classifying low-risk and high-risk P2P-INTs. The NLP models can automatically detect the P2P-INTs risk from Top Management Team (TMT) members' working experience, educational background, and TMT composition with a precision level of more than 75%. |
| first_indexed | 2024-12-18T00:50:57Z |
| format | Article |
| id | doaj.art-60e2333c97634b23820b3cbb89d625e1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T00:50:57Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-60e2333c97634b23820b3cbb89d625e12022-12-21T21:26:41ZengIEEEIEEE Access2169-35362019-01-017725517256010.1109/ACCESS.2019.29197278725485Automatically Detecting Peer-to-Peer Lending Intermediary Risk—Top Management Team Profile Textual Features PerspectiveLei Li0https://orcid.org/0000-0002-3204-6527Yanjie Feng1https://orcid.org/0000-0003-1077-722XYue Lv2Xiaoyue Cong3Xiangling Fu4Jiayin Qi5School of Computer, Beijing University of Posts and Telecommunications, Beijing, ChinaManagement School, Institute of Artificial Intelligence and Change Management, Shanghai University of International Business and Economics, Shanghai, ChinaSchool of Computer, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Computer, Beijing University of Posts and Telecommunications, Beijing, ChinaSoftware School, Beijing University of Posts and Telecommunications, Beijing, ChinaManagement School, Institute of Artificial Intelligence and Change Management, Shanghai University of International Business and Economics, Shanghai, ChinaPeer-to-Peer lending is developing quickly around the world as a new E-finance industry, especially in China. Yet fraudulence and business ceasing of Peer-to-Peer Lending Intermediaries (P2P-INTs) occur frequently, making P2P investors facing serious risk. This paper attempts to explore a bridge connecting managerial research with some most advanced natural language processing (NLP) technologies, and examines the risk assessing power of automatic learning text classifiers based on data of hazard status and top management team profile texts of the P2P-INTs. A risk evaluation model named MULTIPLE NLP Integrated Learning Text Classifier (MUN-LETCLA) based on five NLP techniques and meta-learning is proposed. Then risk classification power of the MUN-LETCLA and the single NLP models is assessed. The results show that the proposed model is effective in classifying low-risk and high-risk P2P-INTs. The NLP models can automatically detect the P2P-INTs risk from Top Management Team (TMT) members' working experience, educational background, and TMT composition with a precision level of more than 75%.https://ieeexplore.ieee.org/document/8725485/Machine learningnatural language processing (NLP)peer-to-peer (P2P) network lendingrisk evaluationtext analysis |
| spellingShingle | Lei Li Yanjie Feng Yue Lv Xiaoyue Cong Xiangling Fu Jiayin Qi Automatically Detecting Peer-to-Peer Lending Intermediary Risk—Top Management Team Profile Textual Features Perspective IEEE Access Machine learning natural language processing (NLP) peer-to-peer (P2P) network lending risk evaluation text analysis |
| title | Automatically Detecting Peer-to-Peer Lending Intermediary Risk—Top Management Team Profile Textual Features Perspective |
| title_full | Automatically Detecting Peer-to-Peer Lending Intermediary Risk—Top Management Team Profile Textual Features Perspective |
| title_fullStr | Automatically Detecting Peer-to-Peer Lending Intermediary Risk—Top Management Team Profile Textual Features Perspective |
| title_full_unstemmed | Automatically Detecting Peer-to-Peer Lending Intermediary Risk—Top Management Team Profile Textual Features Perspective |
| title_short | Automatically Detecting Peer-to-Peer Lending Intermediary Risk—Top Management Team Profile Textual Features Perspective |
| title_sort | automatically detecting peer to peer lending intermediary risk x2014 top management team profile textual features perspective |
| topic | Machine learning natural language processing (NLP) peer-to-peer (P2P) network lending risk evaluation text analysis |
| url | https://ieeexplore.ieee.org/document/8725485/ |
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