Creditworthiness of small and medium enterprises: a fuzzy decision-making approach

Small and medium enterprises (SMEs) play a crucial role in global economies but face significant challenges in accessing credit. Traditional credit assessment models often rely on statistical and artificial intelligence methods, which require extensive financial data usually unavailable for SMEs. This study aims to enhance creditworthiness evaluation by integrating financial and non-financial data using a Fuzzy decision-making approach. We apply this model to 33 Italian SMEs collaborating with a local cooperative credit bank (CCB), leveraging financial and strategic indicators such as internationalization and sustainability. This paper combined the fuzzy decision-making approach with the TOPSIS method, as it is easy to implement. However, our fuzzy-based tool can be integrated with other methods, similar to TOPSIS, such as PROMETHEE, VIKOR, or others. Implemented through a VBA & Excel-based template, the tool allows for flexible and gradual decision-making and accommodates financial and non-financial data. Moreover, the tool's ability to interpret results semantically and its design for processing native semantic data are two of its key strengths. Our empirical research shows that the Fuzzy approach improves credit risk assessment by handling heterogeneous data while maintaining ease of implementation. The proposed approach offers several advantages: its simplicity and modularity make it a valuable tool for CCBs to use as a complementary—rather than a substitute—assessment method alongside the existing system for identifying creditworthy companies. This study contributes to the literature on SME credit evaluation, offering a practical, cost-effective, and adaptable tool for financial institutions, particularly CCBs.