Mining Semantic Soft Factors for Credit Risk Evaluation in Peer-to-Peer Lending

ABSTRACT

While Peer-to-Peer (P2P) lending is rapidly growing, it is also accompanied by high credit risk due to information asymmetry. Besides conventional hard information, soft information also enters into the lending decision process. The descriptive loan texts submitted by borrowers have great potential for exploiting useful soft factors, but also pose great challenges due to the semantic sensitivity to context and the complexity of content representation. We propose a novel text mining method for automatically extracting semantic soft factors from descriptive loan texts. The method maps terms to an embedding space, assembles semantically related terms together into semantic cliques, and then defines semantic soft factors corresponding to the semantic cliques. Empirical evaluation shows that the extracted semantic soft factors contributed to significant improvement on credit risk evaluation in terms of both discrimination performance and granting performance. This work advances our knowledge of soft information indicative of a borrower’s credit risk.

KEYWORDS:

• P2P lending
• descriptive loan text
• credit risk evaluation
• text mining
• soft information
• semantic soft factor
• online lending

Notes

1. http://text2vec.org/index.html

Additional information

Funding

This work was funded by the National Natural Science Foundation of China (Grant Nos. 71731005, 71571059) and the Humanities and Social Science Planning Foundation of Ministry of Education of China (Grant No. 15YJA630010).

Notes on contributors

Zhao Wang

Zhao Wang ([email protected]) is an Assistant Professor at the School of Management, Hefei University of Technology. He received his PhD degree in management science and engineering from that university. His research interests include data mining, and credit evaluation theory and methodology. He has published in such journals as European Journal of Operational Research, Annals of Operations Research, Electronic Commerce Research and Applications, and many others.

Cuiqing Jiang

Cuiqing Jiang ([email protected], [email protected]; corresponding author) is a Professor at the School of Management, Hefei University of Technology. He received his PhD degree in management science and engineering from that University. His research interests include big data analytics and business intelligence, data mining and knowledge discovery, financial technology (Fintech) and information systems. He has published in such journals as European Journal of Operational Research, Information Sciences, Decision Support Systems, International Journal of Production Research, and many others.

Huimin Zhao

Huimin Zhao ([email protected]) is a Professor of Information Technology Management at the Lubar School of Business, University of Wisconsin-Milwaukee. He received his Ph.D. degree in Management Information Systems from the University of Arizona. His research interests include data mining and healthcare informatics. He has published in such journals as MIS Quarterly, Journal of Management Information Systems, Communications of the ACM, IEEE Transactions on Knowledge and Data Engineering, and many others. He serves in the editorial positions at several major scholarly journals.

Yong Ding

Yong Ding ([email protected]) is an Associate Professor at the School of Management, Hefei University of Technology, China. He received his Ph.D. degree from that university. Dr. Ding’s research interests include decision-making theory and methodology, project management, and knowledge management.