EconPapers    
Economics at your fingertips  
 

A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS

Pranith Kumar Roy () and Krishnendu Shaw
Additional contact information
Pranith Kumar Roy: Indian Institute of Technology
Krishnendu Shaw: Indian Institute of Technology

Financial Innovation, 2021, vol. 7, issue 1, 1-27

Abstract: Abstract Small- and medium-sized enterprises (SMEs) have a crucial influence on the economic development of every nation, but access to formal finance remains a barrier. Similarly, financial institutions encounter challenges in the assessment of SMEs’ creditworthiness for the provision of financing. Financial institutions employ credit scoring models to identify potential borrowers and to determine loan pricing and collateral requirements. SMEs are perceived as unorganized in terms of financial data management compared to large corporations, making the assessment of credit risk based on inadequate financial data a cause for financial institutions’ concern. The majority of existing models are data-driven and have faced criticism for failing to meet their assumptions. To address the issue of limited financial record keeping, this study developed and validated a system to predict SMEs’ credit risk by introducing a multicriteria credit scoring model. The model was constructed using a hybrid best–worst method (BWM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Initially, the BWM determines the weight criteria, and TOPSIS is applied to score SMEs. A real-life case study was examined to demonstrate the effectiveness of the proposed model, and a sensitivity analysis varying the weight of the criteria was performed to assess robustness against unpredictable financial situations. The findings indicated that SMEs’ credit history, cash liquidity, and repayment period are the most crucial factors in lending, followed by return on capital, financial flexibility, and integrity. The proposed credit scoring model outperformed the existing commercial model in terms of its accuracy in predicting defaults. This model could assist financial institutions, providing a simple means for identifying potential SMEs to grant credit, and advance further research using alternative approaches.

Keywords: Credit scoring model; SME; Financial institutions; MCDM; BWM; TOPSIS (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://link.springer.com/10.1186/s40854-021-00295-5 Abstract (text/html)

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:fininn:v:7:y:2021:i:1:d:10.1186_s40854-021-00295-5

Ordering information: This journal article can be ordered from
http://www.springer. ... nomics/journal/40589

DOI: 10.1186/s40854-021-00295-5

Access Statistics for this article

Financial Innovation is currently edited by J. Leon Zhao and Zongyi

More articles in Financial Innovation from Springer, Southwestern University of Finance and Economics
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:fininn:v:7:y:2021:i:1:d:10.1186_s40854-021-00295-5