Decisive Lending Rate Analytics for Rural Clntsie’ Clusters

Abstract

Indian banks are evolving with innovative methods in loan approval process, in order to retain their customers and to get rid of competition. Retail customers having good credit score are ready to bargain for less interest rate. In this research, we construct the analytic with the aim of fixing the transparent credit approval system by the banks. This analytic enables the bankers/micro-finance institutions to revise the interest rates of loans based on the creditworthiness of the customers. The model has been evolved based on the data collected from 328 retail loan borrowers. We create clustering procedure after exploring the relationship between interest rate and credit scoring based on vector error correction modelling. The study revealed that, a hundred-point swell in credit score decreases the interest rate by 40 basic points. Hence, the floating interest rate analytic has been determined based on the credit scores as classified based on the clients’ clustering process.