Banking domains such as credit assessments, branch efficiency, electronic banking is tremendous contexts for the broad application of the concepts of business intelligence and its methods include data mining, data warehouses and decision support systems. There are many
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Banking domains such as credit assessments, branch efficiency, electronic banking is tremendous contexts for the broad application of the concepts of business intelligence and its methods include data mining, data warehouses and decision support systems. There are many researches in the field of application of data mining in particular domains of banking, each of which analyzes the different entity of the banking sector, such as customers, facilities, accounts, and so on, but there isn’t research that comprehensively addresses all data mining applications in a bank, it integrates them, extracts and categorizes all banking entities for a variety of analytical applications and ultimately provides an appropriate data model according to the required attributes for the banking domains. Currently, information systems of Iranian banks are being developed for responding to new information needs. In this research by using content analysis method was investigated the content of valid research in the field of banking which was carried out with the data mining approach and by extracting the entities and attributes used in these researches is presented an appropriate data model for data analysis applications in banking. Information technology managers by using this model can assess the status of the bank in terms of the richness of the data needed to conduct data analysis and consider the identified deficiencies in the future development plans of the information systems. After analyzing and evaluating previous researches, 28 entities and 423 attributes were identified and the last entity-relationship model was created. Based on the presented model, a measuring tool was provided as a checklist so that banks can use it to measure their status in terms of the richness of existing data and to measure their readiness from the perspective of the data to do the analysis. To confirm the last data model, were used idea of ten experts by questionnaires and interviews in different sections such as customers and public banking, finance and support, e-banking, credit and corporate affairs, IT domain and international affairs in the bank. Also, using data collected from the researches were presented frequency diagrams of the algorithms, techniques, sampling methods, performance indexes and data mining softwares that used in the researches. To decide which data mining algorithms are most used in different domains as an example.
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