Listening to the investors: A novel framework for online lending default prediction using deep learning neural networks |
| |
| Institution: | 1. School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing, China;2. School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China;3. Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China;1. Faculty of Computer Science and Engineering, Shahid Beheshti University, G.C., Tehran, Iran;2. Faculty of Computer Science and Engineering, University of Zanjan, Zanjan, Iran;3. VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam;1. Information and Communication Technologies, IK4-IKERLAN, J.M. Arizmendiarrieta, 2, 20500 Arrasate;2. Computer Languages and Systems Department, University of the Basque Country UPV/EHU, 649 Postakutxa, 20080 Donostia;1. Electronics and Computing Department, Mondragon Unibersitatea, Arrasate-Mondragón Spain;2. Instituto Universitário de Lisboa (ISCTE-IUL), University Institute of Lisbon, ISTAR-IUL, Av. das Forças Armadas, 1649-026 Lisboa, Portugal;3. Department of Computer Science, University of Vigo, ESEI - Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain;4. CINBIO - Biomedical Research Centre, University of Vigo, Campus Universitario Lagoas-Marcosende, 36310 Vigo, Spain;5. SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur). SERGAS-UVIGO. Hospital Álvaro Cunqueiro Bloque técnico, Estrada de Clara Campoamor, 341, 36312 Vigo, Pontevedra, Spain |
| |
| Abstract: | Online peer-to-peer (P2P) lending has developed dramatically over the last decade in China. But this rapid boom carries potential risks. Investors have incurred incalculable losses due to the recent increase in fraudulent and/or unreliable online P2P platforms. Hence, predicting and identifying potential default risk platforms is crucial at this juncture. To achieve this end, we propose a two-step method which employs a deep learning neural network to extract keywords from investor comments and then utilizes a bidirectional long short-term memory (BiLSTM) based model to predict the default risk of platforms. Experimental results on real-world datasets of about 1000 platforms show that in the keyword extraction phase, our model can better capture semantic features from highly colloquial comment-text and achieve significant improvement over other baselines. Additionally, in the default platform prediction stage, our model achieves an F1 value of 80.34% in identifying potential problem platforms, outperforming four baselines by 23.37%, 5.71%, 8.93%, and 4.98% of improvement and comprehensively verifying the effectiveness of our method. Our study provides an alternative solution for platform default risk prediction issues and validates the effectiveness of investor comments in revealing the risk situation of online lending platforms. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
