A comprehensive method for credit risk assessment of small and medium-sized enterprises based on Asian data

Due to the asymmetry of information between borrowers that are small and medium-sized enterprises (SMEs) and lenders (banks), many banks consider this sector to be risky. It is crucial for banks to be able to distinguish healthy companies from risky ones in order to reduce their nonperforming assets in the SME sector. If they can do this, lending and financing to SMEs through banks will be easier, with lower collateral requirements and lower interest rates. In this chapter, we provide a scheme originally developed by Yoshino and Taghizadeh-Hesary (2014) for assigning credit ratings to SMEs by employing two statistical analysis techniques - principal component analysis and cluster analysis - using 11 financial ratios of 1,363 SMEs in Asia. If used by financial institutions, this comprehensive and efficient method could enable banks and other lending agencies around the world, especially in Asia, to group SME customers based on financial health, adjust interest rates on loans, and set lending ceilings for each group.