IMPROVING THE CREDIT SCORING MODEL OF MICROFINANCE INSTITUTIONS BY SUPPORT VECTOR MACHINE

Asuri Venkata Madhavi, Radhamani .G

Abstract: Credit risk is the most challenging risk to which all the financial institutions are exposed. Credit scoring is the main analytical technique for credit risk assessment of all financial institutions. Microfinance Institutions - one of the financial institutions who would lend money to financially weaker section people are prone to the credit risk due to their nature of service and need proactive credit risk management techniques for their long-term sustainability. Most of the Microfinance Institutions follow traditional statistical techniques for their Credit scoring. The Credit Scoring with data mining techniques in the microfinance industry is relatively a recent application. This paper explores Credit scoring of Microfinance institutions with a novel non-parametric technique called Support Vector Machine. In the proposed model datasets of a Microfinance Institution in Bangalore are compared with other traditional proposed models . The results show that Support vector machines show a higher accuracy rate of classification.

Keywords: Classification, Credit scoring, Microfinance Institutions, Support vector machine (SVM)

DOI: https://doi.org/10.15623/ijret.2014.0319006