Variable Selection for Credit Risk Scoring on Loan Performance Using Regression Analysis

The advancement of information and communication technology has accelerated developments in the field of credit management. This is reciprocated by the introduction of data analytics to process relevant information that could be useful specifically in financial granting decisions. With this, the researcher presents a research-in-progress of designing a risk analysis and recommendation system for the Department of Science and Technology VII Small & Medium Enterprise Technology Upgrading Program (DOST VII-SETUP). Its main feature is focused on credit risk analysis. To develop the application, selected variables to be used for credit scoring is identified based on the DOST Administrative Order No. 002 on Revised Small Enterprises Technology (SET-UP) Guidelines and 9-year historical data on granted loan projects from 2008-2016. With the use of tableau software, a data mining process was executed utilizing linear regression and trend model visualization for analysis. As the data on selected variables are validated, a proposed decision matrix on credit scoring has been developed. This leads to the recommendation on the development of the credit risk analysis and recommendation system by computing the center of gravity of each score through the fuzzy logic algorithm.