The investigation of accuracy level prediction of Fintech customers loyalty by using data mining algorithm

Document Type : Research Paper


1 Department of Information Technology Management, Qeshm Branch, Islamic Azad University, Qeshm, Iran

2 Department of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

3 Department of Industrial Engineering, Faculty of Engineering, Khatam University, Tehran, Iran

4 Department of Accounting, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran


The emergence of mobile applications is forcing ambitious companies hoping to build loyalty for customers’ brands to rush towards marketing their brand applications. The present research was conducted with the aim of classifying loyal customers and measuring their loyalty level using data mining algorithms. The present research method is based on applied-descriptive and the statistical population included the customers of Asan Pardakht Company which were considered number ten thousand people and with the number of 700,000 transactions. These customers were separated by clustering operation and classified for performing different tests. By using the data of Fintech customers of Asan Pardakht Company, it was attempted by using the decision tree algorithm, in addition, to identifying active customers, to implement this algorithm, a way is made in order to increase customer loyalty and ultimately increase their profitability and create satisfaction among managers. In the present research, by implementing the different stages of Crisp methodology, clustering and testing different artificial intelligence algorithms, the most useful algorithm in order to identify the best customers and also to make them loyal and policies and implementable programs to be formulated in order to increase the satisfaction percentage and finally customers’ loyalty was explained and mentioned.


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Volume 14, Issue 6
June 2023
Pages 265-271
  • Receive Date: 27 April 2022
  • Revise Date: 20 June 2022
  • Accept Date: 29 July 2022