Behnam Golshahi; Nahid Dorostkar Ahmadi; Nahid Sadeghi
Abstract
This study has the goal of assessing and prioritizing self-serving technologies with a combined approach to continuous usage of electronic banking services. To achieve this goal we used literature review and interviewing banking experts to determine parameters affecting continuous usage of ...
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This study has the goal of assessing and prioritizing self-serving technologies with a combined approach to continuous usage of electronic banking services. To achieve this goal we used literature review and interviewing banking experts to determine parameters affecting continuous usage of self-serving technologies. And to prioritize these parameters we used paired comparison analysis questionnaires and fuzzy analytic hierarchy process.Validity of questionnaire was attained by content method and its stability was attained by calculating the rate of compatibility of aggregation matrix of experts’ opinions. Statistical population of this research includes 30 managers and deputy managers of selected Saman Banks’s branches in the region of northern Tehran which by snowball sampling method 12 of them were selected to participate in this study. Data analysis were done using fuzzy AHP and using MATLAB software. Analysis of data showed that self-serving services have different weighs according to three parameters of customer value, quality of service and customer readiness in which POS devices (0.662 weigh) have the most usability and importance from the view of banking experts. Other services rank as ATM (0.181). Mobile banking services (0.101) and Internet bank (0.056) respectively. In addition ranking of self-serving technologies according to 14 indexes also justifies the previous results.
Mohammad’reza Gholamian; Azimeh Mozafari
Abstract
Management and evaluation of valuable customers, is one of the most important banking factors to reduce costs and increase profitability. In recent decades, many researchers have studied on the analysis of the customer attributes to evaluate value of them using data mining techniques and decision tree ...
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Management and evaluation of valuable customers, is one of the most important banking factors to reduce costs and increase profitability. In recent decades, many researchers have studied on the analysis of the customer attributes to evaluate value of them using data mining techniques and decision tree is one of the most widely used data mining algorithms in the field. Since this algorithm for built tree, considers only one attribute at a time to test each node and ignores the dependency between attributes, therefore, required maximum memory is increased. To solve this problem, in this research a method is proposed to improve the decision tree using neural network to explore the dependency between the attributes based on reduction in required maximum memory that is used based on RFM model to predict customer values. Results show that the proposed method using dependencies between attributes will predict the new customer values by less maximum memory compare to the basic method
