Behavioral Model for Banking Customer based on Neural Network with Time Series

Abstract: Data mining is an essential tool for any banking CRM strategy to be successful. It not only recognizes patterns to make predictions, but can also highlight available opportunities. With the many advantages and new avenues that it offers, this is one tool that no bank can ignore if it wants to retain its customers and stand out on a highly competitive industry. This study presents a new stage frame work of customer behavior analysis that integrated a neural network with the help of time series algorithm. The time series mining function provides algorithms that are based on different underlying model assumptions with several parameters. The learning algorithms try to find the best model and the best parameter values for the given data. In time series algorithm which roles a main part o calculate a detailed forecast including seasonal behavior of the original tije series. The autoregressive part of the algorithm uses weighed previous values while the moving average part weights the previously assumed errors of the time series. The objective of my project of to identify the most value customer in the banking databases using decomposing the data, and their loyalty. Time series data is helpful characteristics of customer and facilitates marketing strategy development.

Keywords: Data Mining, Neural Network.