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Fuzzy clustering analysis for the loan audit short texts

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Abstract

In China, post-loan management is usually executed in the form of a visit survey conducted by a credit manager. Through a quarterly visit survey, a large number of loan audit short texts, which contain valuable information for evaluating the credit status of small and micro-enterprises, are collected. However, methods for analysing this type of short text remain lacking. This study proposes a method for processing short loan audit texts called fuzzy clustering analysis (FCA). This method first transforms short texts into a fuzzy matrix through lexical analysis; it then calculates the similarity between records based on each fuzzy matrix and constructs an association graph with this similarity. Finally, it uses a prism minimum spanning tree to extract clusters based on different \({\alpha }\) cuts. Experiments using actual data from a commercial bank in China revealed that the FCA yields suitable clustering results when handling loan audit briefs. Moreover, it exhibited superior performance compared to BIRCH, k-means, and fuzzy c-means.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (Grant No. 72101279).

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Authors and Affiliations

  1. School of Management Science and Engineering, Central University of Finance and Economics, Beijing, 100081, China

    Lu Han, Zhidong Liu & Zhuangyi Zhang

  2. School of Information Engineering, Yangzhou University, Yangzhou, 225127, China

    Jipeng Qiang

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  1. Lu Han
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LH and JQ wrote the main manuscript text, ZL proposed research ideas, and ZZ revised the manuscript and summarized literatures. All authors reviewed the manuscript.

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Correspondence to Jipeng Qiang.

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Han, L., Liu, Z., Qiang, J. et al. Fuzzy clustering analysis for the loan audit short texts. Knowl Inf Syst 65, 5331–5351 (2023). https://doi.org/10.1007/s10115-023-01943-1

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