Classification of mobile banking users by data mining approach: Comparison between artificial neural networks and naïve bayes techniques

In recent decay development of different technologies is dramitically high and it influences in services. One of fields which is affected from information technology is bankining industry. Wireless technology is one of the information technology fields that has a greate development in recent years which resualt of that is mobile banking service. Banking industry is a smple that uses data mining technique. Data mining is kind of exploring knowledge to solve a special problem. In this research 232817 data is used to find some models by artificial neural networks and naïve bayes techniques in according with customer's attributes. Furtheremore result of this research help to classify customers who use mobile banking service and then the bank can offer the service to somebody who are in this classification but donot use this service. So in this way the bank can attract more customers, maintain its customers, and keep high customer's satisfaction. Also the research reveals that artificial neural networks is more accurate than naivie bayes and the research's hypothesis is proved.