Assessing and Predicting Small Enterprises’ Credit Ratings: a Multicriteria Approach

EasyChair Preprint 7946

Abstract

Credit rating plays a crucial role in helping financial institutions make their lending decisions and in reducing the financial constraints of small enterprises. However, small enterprises have made it difficult for financial institutions such as commercial banks to determine their credit risk precisely, thus creating salient loan difficulties, because of the short duration, high frequency, urgent credit demand, and small amount of their loans. In an attempt to relieve the financing difficulty of small enterprises, this paper develops a new approach for small enterprises’ credit risk assessment by combining high dimensional attribute reduction methods with fuzzy decision-making methods. Based on 687 small enterprises in a regional commercial bank of China, we find 17 indicators that have significant impact on the default risk of small enterprises. Then, it utilizes TOPSIS together with fuzzy C-means to grade the credit ratings of enterprises requesting loans. With the dual test of default discrimination and ROC curve, the prediction accuracy of the established indicator system has reached 85.40% and 90.09% respectively, indicating the strong default discrimination of this rating system and its practicability in commercial banks and other financial institutions.

Keyphrases: Fuzzy C-Means, credit rating, default risk, small enterprises

@booklet{EasyChair:7946,
  author    = {Baofeng Shi},
  title     = {Assessing and Predicting Small Enterprises’ Credit Ratings: a Multicriteria Approach},
  howpublished = {EasyChair Preprint 7946},
  year      = {EasyChair, 2022}}