Assessment of Customer Credit through Combined Clustering of Artificial
  Neural Networks, Genetics Algorithm and Bayesian Probabilities
  Today, with respect to the increasing growth of demand to get credit from the
customers of banks and finance and credit institutions, using an effective and
efficient method to decrease the risk of non-repayment of credit given is very
necessary. Assessment of customers' credit is one of the most important and the
most essential duties of banks and institutions, and if an error occurs in this
field, it would leads to the great losses for banks and institutions. Thus,
using the predicting computer systems has been significantly progressed in
recent decades. The data that are provided to the credit institutions' managers
help them to make a straight decision for giving the credit or not-giving it.
In this paper, we will assess the customer credit through a combined
classification using artificial neural networks, genetics algorithm and
Bayesian probabilities simultaneously, and the results obtained from three
methods mentioned above would be used to achieve an appropriate and final
result. We use the K_folds cross validation test in order to assess the method
and finally, we compare the proposed method with the methods such as
Clustering-Launched Classification (CLC), Support Vector Machine (SVM) as well
as GA+SVM where the genetics algorithm has been used to improve them.
