Small and Medium Enterprises (SMEs) have played a significant role in the development of any economy. However, easy access to finance from financial institutions is a prime challenge for them. Similarly, financial institutions also face difficulties while selecting the potential SMEs for granting credit. The SMEs are often seen as unorganized in terms of financial data as compared to large corporate sectors. The credit risk assessment based on unorganized financial data is a challenge for financial institutions. Most of the existing models used regression to predict the possibility of default of SMEs. However, the regression model may not perform well with limited data points and missing data. The problem can be solved by using a multi criteria decision‐making (MCDM) model. Credit scoring, especially addressing the SMEs, has been infrequently reported in the archived literature. To fill the gaps of literature, the present study proposes a credit scoring model applying the hybrid analytic hierarchy process‐technique for order of preference by similarity to ideal solution (AHP‐TOPSIS) technique. The study has been carried out in three stages. In the first stage, credit rating criteria and sub‐criteria have been identified from the literature review and taking opinions from experts. In the second stage, weights of criteria and sub‐criteria have been calculated using AHP. Finally, in the third stage, weights calculated by AHP have been used in TOPSIS to determine the credit score. The effectiveness of the proposed model has been illustrated through a case study. Further, the results of the proposed model are compared with the commercially available ratings. The proposed model may be a low‐cost alternative for financial institutions for credit scoring of SMEs. Further, the model has the advantage of customization as per the needs of the financial institutions. The suggested model can help the managers to identify the potential SMEs for granting credit.

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.

No abstract

No abstract

Mobile technology has revolutionised various business processes. Banking is one of them. Traditional banking operations are gradually changing with the introduction of efficient mobile technologies. Mobile banking (m-banking) has recently emerged as an innovative banking channel that provides continuous real-time customer service. It is expected that the market for m-banking will expand in the near future. There are currently various types of m-banking applications in the market. However, ranking and selecting efficient applications is difficult due to the involvement of multiple factors. As of now, very few studies have reported the m-banking application selection framework, left scope for further research. The current study proposes an m-banking application selection model based on a combined fuzzy best–worst method (fuzzy-BWM) and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (fuzzy-TOPSIS). The research was carried out in several stages, beginning with the identification of potential factors and progressing to pair-wise comparisons and the final ranking of the applications. The fuzzy set theory was applied to handle the ambiguity of the decision maker. In the first stage, fuzzy-BWM was used to determine the weight of the factors. Further, fuzzy-TOPSIS was applied to rank the m-banking applications. The present study has adopted a new fuzzy BWM, which differs significantly from the existing fuzzy-BWM, to solve the nonlinearity problem of optimisation. The applicability of the proposed model has been demonstrated through a real-life case study. The efficacy of the model has been further examined by performing a sensitivity analysis. The study observed application functionality, convenience, and performance expectancy as significant factors in selecting an m-banking application, followed by performance quality, security, and compatibility. The proposed model can assist financial institutions and customers to overcome the challenges of choosing an appropriate m-banking application. The proposed model can be used to benchmark the m-banking applications in the market.