Credit risk assessment for SMEs in developing economies

Abstract

SMEs are important in the development of the economy, creation of jobs and innovation in the developing economies. Irrespective of the importance, SMEs also experience great problems when they seek formal credit because of information asymmetry, lack of collateral, and poor financial records. Effective credit risk evaluation is thus critical to the financial institution intending to finance SMEs with minimal risk of default. This paper looks at the determining factors and methodology applied in evaluation of credit risk among SMEs in the third-world economies. The studies consider the conventional methods of credit evaluation that include financial ratio analysis and statistical credit scoring models, as well as the analysis of the emerging methods of credit evaluation that include machine learning, fintech-based credit scoring, and alternative data analytics. The research indicates that a combined approach of financial indicators and non-financial and behavioral variables enhances the precision in the credit risk prediction. The results indicate that hybrid credit risk assessment models that involve integration of conventional banking models and innovative data-driven approaches may play a vital role in improving the lending decision-making and improving access to credit by SMEs. The research also highlights the need to enhance credit information infrastructure and financial infrastructure to enable more inclusive and effective financing of SMEs.