Credit decision support methodology for Micro, Small and Medium Enterprises (MSMEs) : Indonesian cases

Abstract

This research is aimed at constructing an objective and accurate credit risk assessment method for Micro, Small and Medium Enterprises in Indonesia. Credit data of three sample banks is structured using eXtensible Markup Language and Database Structure Model for analysis purposes. Selected data mining techniques are applied to perform credit risk classification based on quantitative and text-based qualitative information. This fills the gap in previous studies where text-based qualitative information was excluded from the models.