Fuzzy clustering analysis for the loan audit short texts

Lu Han, Zhidong Liu, Jipeng Qiang, Zhuangyi Zhang

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.