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

Baofeng Shi ()
Additional contact information
Baofeng Shi: College of Economics and Management, Northwest A&F University

A chapter in Novel Financial Applications of Machine Learning and Deep Learning, 2023, pp 125-149 from Springer

Abstract: Abstract Credit ratings play a key role in helping financial institutions to make loan decisions and to reduce the financial constraints on small and medium-sized enterprises. However, small enterprises have made it difficult for financial institutions such as commercial banks to accurately determine their credit risk, creating salient loan difficulties, due to the short duration, high frequency, urgent demand for credit, and small amount of their loans. In order to alleviate the difficulties of financing small businesses, this paper develops a new approach for the assessment of credit risk in small enterprises 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 a 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. The standard discrimination and ROC curve dual tests resulted in the prediction accuracy of the standard indicator system reaching 85.40 percent and 90.09 percent, respectively, indicating the strong default discrimination of this rating system and its practicability in commercial banks and other financial institutions.

Keywords: Credit rating; Default risk; Fuzzy C-means; Small enterprises (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-031-18552-6_8

Ordering information: This item can be ordered from
http://www.springer.com/9783031185526

DOI: 10.1007/978-3-031-18552-6_8

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().