Listening to the investors: A novel framework for online lending default prediction using deep learning neural networks. Issue 4 (July 2020)

• Record Type: Journal Article
• Authors: Fu, Xiangling; Ouyang, Tianxiong; Chen, Jinpeng; Luo, Xiaopeng
• Is Part Of: Information processing & management. Volume 57:Issue 4(2020:Jul.)
• Publication Date: 2020-07
• Subjects: Online P2P (peer-2-peer) lending -- Online P2P platform default prediction -- deep learning -- keyword extraction; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2020.102236
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: In terms of keywords extraction, we propose an improved ELMo-BiLSTM-CNNCRF sequence labeling model. For predicting default risk of P2P online platform, this study develops a deep learning model to achieve sounder results and provides some new insights to evaluate platform risk from investors' perspective. 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.