A Meta Path Based Evaluation Method for Enterprise Credit Risk

TL;DR

This paper proposes a novel meta path-based evaluation method leveraging information networks to assess credit risk of SMEs using secondary data, addressing data scarcity issues and improving risk identification accuracy.

Contribution

It introduces a new meta path feature for SME credit risk evaluation, enabling multi-perspective analysis from secondary data sources.

Findings

Effective in identifying SMEs with credit risks

Demonstrates the utility of meta path features in credit evaluation

Enhances assessment accuracy with limited primary data

Abstract

Nowadays small and medium-sized enterprises have become an essential part of the national economy. With the increasing number of such enterprises, how to evaluate their credit risk becomes a hot issue. Unlike big enterprises with massive data to analyze, it is hard to find enough information of small enterprises to assess their financial status. Limited by the lack of primary data, how to inference small enterprises' credit risk from secondary data, like information of their upstream, downstream, parent, and subsidiary enterprises attracts big attention from industry and academy. Targeting on accurately evaluating the credit risk of the small and medium-sized enterprise (SME), in this paper, we exploit the representative power of Information Network on various kinds of SME entities and SME relationships to solve the problem. A novel feature named meta path feature proposed to measure the credit risk, which makes us able to evaluate the financial status of SMEs from various perspectives. Experiments show that our method is effective to identify SMEs with credit risks.