The development of bank applications for debtors' selection by using Naïve Bayes classifier technique

Abstract

The purpose of this study is to create an application which functions automatically with high accuracy when analyzing bank customer data. This needed due to non-perforMing loans occurring frequently caused by the inaccuracy of credit analysts in the assessment of creditworthiness. This can be seen in the incident occurred in a public bank located in Bandung. This bank does not have the database that serves to accommodate data history and the method used in assessing creditworthiness is merely based on the simple statistical analysis. This leads to reduced accuracy and speed in the decision-making process. This research applies Naïve Bayes Classifier (NBC) method, a Data Mining technique. This helps credit analysts to select customers who are truly eligible to be given credit so that non-perforMing loan can be avoided. NBC calculates the probability of one class from each group of attributes and deterMines which class is most optimal. The accuracy of the NBC sampling test from 501 data is 94% compared to the decision made by a credit analyst. It can be concluded that this application is very helpful for credit analysts in recommending customers who are eligible for a loan to the bank's decision maker.