نوع مقاله : مقاله علمی-پژوهشی
عنوان مقاله English
نویسندگان English
In today's world, small and medium enterprises (SMEs) are recognized as the driving force of economic growth and job creation; however, limited access to appropriate financial facilities remains one of the main obstacles to their development. Traditional banking approaches to granting credit, due to the long review stages and inefficiency in accurately measuring risk, no longer meet the rapid and changing needs of these enterprises. The present study is an applied research with a mixed method (qualitative-quantitative) that aims to design an intelligent framework to improve the financing process of SMEs and simultaneously reduce the credit risks of banks. In the qualitative phase, using the meta-synthesis technique, key factors affecting the success of financing were identified. In the quantitative part, the actual data of the facilities granted by Tejarat Bank to 1073 SMEs over a five-year period were analyzed and predictive modeling was performed using machine learning algorithms including linear regression, decision tree, k-NN, SVM and artificial neural networks. The qualitative findings led to the extraction of four main dimensions: business characteristics, management and operational strategies, financial-credit conditions and environmental factors. The quantitative results showed that the artificial neural network (ANN), by utilizing the full set of features, showed the best performance with an average accuracy of 95.75% and high stability in predicting the success of granting facilities. Also, the dimension of “financial and credit status” was identified as the most determining group of characteristics in Tejarat Bank’s credit decision-making. The final conceptual model of this research depicts the relative weight of different dimensions in the SME financing decision-making process. The overall results suggest that machine learning algorithms – particularly neural networks – have significant potential to improve credit assessment accuracy, reduce default risk, facilitate SMEs’ access to finance, increase productivity, and reduce banks’ operating costs.
کلیدواژهها English
