Classification of Internet banking customers using data mining algorithms
Classifying customers using data mining algorithms, enables banks to keep old customers loyality while attracting new ones. Using decision tree as a data mining technique, we can optimize customer classification provided that the appropriate decision tree is selected. In this article we have present...
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| Format: | Article |
| Language: | fas |
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University of Tehran
2014-03-01
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| Series: | Journal of Information Technology Management |
| Subjects: | |
| Online Access: | https://jitm.ut.ac.ir/article_50051_267cbcc51cdf0588c44d046e3d143039.pdf |
| _version_ | 1811248499436552192 |
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| author | Reza Radfar Navid Nezafati Saeid Yousefi Asli |
| author_facet | Reza Radfar Navid Nezafati Saeid Yousefi Asli |
| author_sort | Reza Radfar |
| collection | DOAJ |
| description | Classifying customers using data mining algorithms, enables banks to keep old customers loyality while attracting new ones. Using decision tree as a data mining technique, we can optimize customer classification provided that the appropriate decision tree is selected. In this article we have presented an appropriate model to classify customers who use internet banking service. The model is developed based on CRISP-DM standard and we have used real data of Sina bank’s Internet bank. In compare to other decision trees, ours is based on both optimization and accuracy factors that recognizes new potential internet banking customers using a three level classification, which is low/medium and high. This is a practical, documentary-based research. Mining customer rules enables managers to make policies based on found out patterns in order to have a better perception of what customers really desire. |
| first_indexed | 2024-04-12T15:29:13Z |
| format | Article |
| id | doaj.art-6f3ada877a864215a9347fd96c69f94e |
| institution | Directory Open Access Journal |
| issn | 2008-5893 2423-5059 |
| language | fas |
| last_indexed | 2024-04-12T15:29:13Z |
| publishDate | 2014-03-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj.art-6f3ada877a864215a9347fd96c69f94e2022-12-22T03:27:10ZfasUniversity of TehranJournal of Information Technology Management2008-58932423-50592014-03-0161719010.22059/jitm.2014.5005150051Classification of Internet banking customers using data mining algorithmsReza Radfar0Navid Nezafati1Saeid Yousefi Asli2Associate Prof., Faculty of Management and Economics, Science and Research Branch Islamic Azad University, Tehran, IranAssistant Prof., Faculty of Management and Accounting, Shahid Beheshti University, Tehran, IranMSc. in Information Technology Management, Azad University, E Campus, Tehran, Iran.Classifying customers using data mining algorithms, enables banks to keep old customers loyality while attracting new ones. Using decision tree as a data mining technique, we can optimize customer classification provided that the appropriate decision tree is selected. In this article we have presented an appropriate model to classify customers who use internet banking service. The model is developed based on CRISP-DM standard and we have used real data of Sina bank’s Internet bank. In compare to other decision trees, ours is based on both optimization and accuracy factors that recognizes new potential internet banking customers using a three level classification, which is low/medium and high. This is a practical, documentary-based research. Mining customer rules enables managers to make policies based on found out patterns in order to have a better perception of what customers really desire.https://jitm.ut.ac.ir/article_50051_267cbcc51cdf0588c44d046e3d143039.pdfData Miningdecision treeClassificationE-Banking |
| spellingShingle | Reza Radfar Navid Nezafati Saeid Yousefi Asli Classification of Internet banking customers using data mining algorithms Journal of Information Technology Management Data Mining decision tree Classification E-Banking |
| title | Classification of Internet banking customers using data mining algorithms |
| title_full | Classification of Internet banking customers using data mining algorithms |
| title_fullStr | Classification of Internet banking customers using data mining algorithms |
| title_full_unstemmed | Classification of Internet banking customers using data mining algorithms |
| title_short | Classification of Internet banking customers using data mining algorithms |
| title_sort | classification of internet banking customers using data mining algorithms |
| topic | Data Mining decision tree Classification E-Banking |
| url | https://jitm.ut.ac.ir/article_50051_267cbcc51cdf0588c44d046e3d143039.pdf |
| work_keys_str_mv | AT rezaradfar classificationofinternetbankingcustomersusingdataminingalgorithms AT navidnezafati classificationofinternetbankingcustomersusingdataminingalgorithms AT saeidyousefiasli classificationofinternetbankingcustomersusingdataminingalgorithms |