Credit Risk Evaluation Based on Text Analysis

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Author(s): Shuxia Wang (Beijing Institute of Petrochemical Technology, China), Yuwei Qi (Peking University, China), Bin Fu (Peking University, China)and Hongzhi Liu (Peking University, China)
Copyright: 2018
Pages: 12
Source title: Intelligent Systems: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-5643-5.ch080

Keywords: Artificial Intelligence / Computer Engineering / Computer Science & IT / Information Science Reference

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Abstract

The main difficulty of credit risk evaluation is to evaluate borrowers' willingness of repayment, which is a subjective factor depending on the thoughts and ideas of borrowers. Text description is a kind of human behavior which reflects the mental process of writers. The authors identify the characteristics of borrowers from their text descriptions and further use them to evaluate the credit risk of loans. Experimental results show that: (1) textual information is a good choice when traditional financial information is missing. The authors can achieve similar accuracy using only textual information as traditional methods which use financial information and credit information from the third party. (2) Textual information is a good complementary information source to traditional financial information sources. Using textual information can improve the performance of credit risk evaluation system when combined with traditional financial information.

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Credit Risk Evaluation Based on Text Analysis

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