Logistical Regression Model For Predicting Small And Medium Enterprises’ (Smes) Credit Risk For Commercial Banks In Kenya.

Abstract

Small and medium enterprises play a very vital role in the growth of any country economy. They provide employment to both the owner(s) and the employee(s). However, their growth has been hampered by lack of capital for expansion and operational expenses. Commercial banks in Kenya provide the main source of funding to these SMEs through the various loan products they offer. As a result of these borrowings, banks have been exposed to default risk which affects their profitability. The purpose of this research is help to generate a predictive model for accessing SMEs probability of default. Convenient sampling was used for selecting the entire population consisting of commercial banks in Kenya thereby utilising the census as opposed to sampling criterion. The study utilises the KDD model to direct the development of the research study. The collected data will be analysed using R software. Additionally, the study used logistic regression to testing the statistical significance of the relationship between variables with a p value of P > 0.05 considered significant, and P ≤ 0.05 considered not statically significant.