A Data Mining Method for Satisfaction and Confidence of the Bank Customers
محورهای موضوعی : journal of Artificial Intelligence in Electrical Engineering
Parisa Allahverdizadeh
1
(Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran.)
Saeid Taghavi Afshord
2
(Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran)
کلید واژه: Trust, Bank, Data mining, Classification, Customers,
چکیده مقاله :
Trust is the main concern of the Bank's customers regarding electronic and Internet services. The trust of both customers is logically and experimentally important to each other, and banks need to take more steps as service providers to maintain their customers. It is necessary to increase the factors affecting the satisfaction and reliability of customers in banks using data mining. In this paper, we examine the factors affecting the increase of customers' confidence in banking and Internet banking services and the impact of any perceived credit factor by public and private banks, service providers, and infrastructure providers in electronic banking. The presented method is based on scientific data mining algorithms such as clustering and classification of the decision tree J48 and the neural network, as well as a quick and practical application of the miner. Data are analyzed using a questionnaire with the bank customers of 25 Tejart bank branches in Tehran. The experimental results demonstrate that the accuracy of the decision tree classification algorithm is 84.04 and the neural network is 72.3%.
Trust is the main concern of the Bank's customers regarding electronic and Internet services. The trust of both customers is logically and experimentally important to each other, and banks need to take more steps as service providers to maintain their customers. It is necessary to increase the factors affecting the satisfaction and reliability of customers in banks using data mining. In this paper, we examine the factors affecting the increase of customers' confidence in banking and Internet banking services and the impact of any perceived credit factor by public and private banks, service providers, and infrastructure providers in electronic banking. The presented method is based on scientific data mining algorithms such as clustering and classification of the decision tree J48 and the neural network, as well as a quick and practical application of the miner. Data are analyzed using a questionnaire with the bank customers of 25 Tejart bank branches in Tehran. The experimental results demonstrate that the accuracy of the decision tree classification algorithm is 84.04 and the neural network is 72.3%.
