Microfinance interest rate prediction and automate the loan application

Abstract

In the Sri Lankan context, microfinance is the main banking service provider for the 'unbankable' people who live in rural areas. Due to the informal nature and high-interest rate, microfinance continues to operate in a falling field. In addition, in the appraisal stage, rural borrowers tend to make mistakes while filling out loan applications causing default situations. As a result, microfinance institutions demotivate and people shifting away from traditional forms of borrowing to informal forms is socially problematic. Therefore, in order to address those issues, the researchers collected primary data through ABC bank and analyzed data relating to 10000 random borrowers' records and set of loan forms. The purpose of this research is to automate paper-based loan applications to recognize handwritten characters with the use of information technology through image processing techniques and analyze customers' determinant factors to predict interest rates for microfinance facilities. The finding of our study shows that Regression analysis models can gain the best result in predicting the interest rate. Together Ridge regression analysis and XGBoost regression models gave the most accurate results compared to other models in interest rate prediction. By using this interest rate prediction, microfinance institutions can offer a suitable interest rate that is convenient for the loan borrower. The application automation reduces the paperwork and the manual effort needed to process and increases data accuracy. The developed model can enhance the repayment performance of microfinance firms and prevent defaults by borrowers at the loan appraisal stage.