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).
