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