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

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.

A recent review on optimisation methods applied to credit scoring models

“…To establish a CS, the Operational Research models, such as Linear Programming, Quadratic Programming and Multiple-Criteria Decision-Making are also used in CSMs (Marqués et al. , 2013; Roy and Shaw, 2021b). Evolutionary Computation, Artificial Intelligence, Data Mining and Machine-Learning techniques describe credit risk with greater precision (Breeden, 2021; Xia et al.…”

“…The researchers integrated the Analytic Hierarchy Process (AHP) and the Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) for AHP-TOPSIS. Roy and Shaw (2021b) developed a system to predict SMEs’ credit risk by introducing a multi-criteria model formulated using a hybrid method that combines TOPSIS and Best-Worst Method (BWM). Xia et al.…”

Bütünleşik BWM ve TOPSIS yöntemleri kullanılarak OPEC üyesi ülkeler için kurumsal gelişmişlik analizi

“…karar problemlerinde de uygulanmaktadır. Ek olarak, literatürde BWM ve TOPSIS yöntemlerinin birlikte kullanıldığı çalışmalara da erişmek mümkündür (You, Guo, Zhao, Zhao, 2017;Gupta & Barua, 2018;Akyüz, Tosun, Salih, 2020;Roy & Shaw, 2021).…”

Economic Challenges And The Potential Threat Of A Debt Trap In Asia

“…The credit ratings of most financial institutions in Thailand are risk-based, a legacy of the restructuring of the Thai financial system after the 1997 Thai financial crisis (Noman, Hassan, Pervin, Isa, & Sok-gee, 2022). However, risk-based credit ratings in a financial system where information does not fully flow can 'limit' potential business access to credit (Roy & Shaw, 2021). Addressing information imperfections can be achieved by incentivizing borrowers to disclose their intention to borrow (Mhlanga, 2021).…”