Early Warning Signals on Credit Risk Mitigation among SME’s in Malaysia

Abstract

By combining financial and non-financial characteristics, the research creates a credit risk mitigation model for SME's. These variables were used to analyse the impact of owner education level, gender, and business age as an early warning signal to the credit risk mitigation. To analyse the impact of owner education level, gender, and business age as an early warning signal to the credit risk mitigation, the research developed a credit risk mitigation model for SME's by combining financial and non-financial characteristics. Utilizing all relevant information, a Multiple Discriminant Analysis (MDA) model was applied. 400 observations make up the final sample for the estimation model, of which half are distressed and non-distressed SME’s, for the years 2015 to 2020. The prediction models perform relatively well in the financial and non-financial variables. This evidence shows that the models serve as efficient early warning signals and can thus be beneficial for monitoring and evaluation of credit risk.