Integrated Multivariate Segmentation Tree for Heterogeneous Credit Data Analysis in Small- and Medium-Sized Enterprises

TL;DR

This paper introduces the integrated multivariate segmentation tree (IMST), a novel framework that combines textual and financial data to enhance credit evaluation accuracy for SMEs, outperforming traditional models.

Contribution

The paper presents a new integrated decision tree model that effectively incorporates textual and financial data, improving interpretability and accuracy in SME credit assessment.

Findings

IMST achieved 88.9% accuracy on SME data.

Outperformed baseline decision trees, SVMs, and neural networks.

Demonstrated improved interpretability and efficiency.

Abstract

Traditional decision tree models, which rely exclusively on numerical variables, often face challenges in handling high-dimensional data and are limited in their ability to incorporate textual information effectively. To address these limitations, we propose the integrated multivariate segmentation tree (IMST), a comprehensive framework designed to improve credit evaluation for small- and medium-sized enterprises (SMEs) by integrating financial data with textual sources. This method comprises three core stages: (1) transforming textual data into numerical matrices through matrix factorization, (2) selecting salient financial features using Lasso regression, and (3) constructing a multivariate segmentation tree based on either the Gini index or entropy, with weakest-link pruning applied to control model complexity. Experimental results based on a dataset of 1,428 Chinese SMEs demonstrated that IMST achieved an accuracy rate of 88.9%, surpassing both baseline decision trees (87.4%) and conventional models such as support vector machines and neural networks. Furthermore, the proposed model demonstrated superior interpretability and computational efficiency, featuring a more streamlined architecture and improved risk detection capabilities.