Implementation of Credit Classification Using Competitive Neural Tree

Abstract

Credit classification is a system that determines credit applicants, either “good credit” one that is likely to repay financial obligation or “bad credit” one who has high possibility of defaulting on financial obligation, by analyzing customer’s data.In a credit classification system, an applicant’s data are assessed and evaluated, like financial status, preceding past payments and company background to distinguish between a “good” and a “bad” applicant. This is usually done by taking a sample of past customers.Many models and algorithms have been applied to support credit classification, including statistical, genetic algorithm and neural networks. Neural network and decision trees are widely used in various classification task that is required no knowledge on the data. The advantages of neural network and decision trees are combined in Competitive Neural Trees(CNeT).This system is implemented Credit Classification using Competitive Neural Tree. There are 1000 records to implement this system. In each record includes 20 attributes. This system displays one of two classes of Credit (good or bad credit).