Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending

Cuiqing Jiang, Zhao Wang, Ruiya Wang () and Yong Ding
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Cuiqing Jiang: Hefei University of Technology
Zhao Wang: Hefei University of Technology
Ruiya Wang: Hefei University of Technology
Yong Ding: Hefei University of Technology

Annals of Operations Research, 2018, vol. 266, issue 1, No 20, 529 pages

Abstract: Abstract Predicting whether a borrower will default on a loan is of significant concern to platforms and investors in online peer-to-peer (P2P) lending. Because the data types online platforms use are complex and involve unstructured information such as text, which is difficult to quantify and analyze, loan default prediction faces new challenges in P2P. To this end, we propose a default prediction method for P2P lending combined with soft information related to textual description. We introduce a topic model to extract valuable features from the descriptive text concerning loans and construct four default prediction models to demonstrate the performance of these features for default prediction. Moreover, a two-stage method is designed to select an effective feature set containing both soft and hard information. An empirical analysis using real-word data from a major P2P lending platform in China shows that the proposed method can improve loan default prediction performance compared with existing methods based only on hard information.

Keywords: P2P lending; Default prediction; Soft information; Topic model (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (48)

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DOI: 10.1007/s10479-017-2668-z

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