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Exploration and Discovery of Customer Behavioral Patterns in Electronic Banking Services Using a Data Science Approach
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Sayyed Mohammad Sadegh Davoudi   , Sayyed Kazem Ebrahimi * , Abbas Toloui Ashlaqi |
| Department of Accounting, Faculty of Economics, Management and Administrative Sciences, Semnan University , kebrahimi@semnan.ac.ir |
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Abstract: (6 Views) |
| Banks play a key role in the Iranian economy, and with the expansion of electronic banking, the use of banking services has become an integral part of people's economic lives. One of the important challenges for bank managers is to identify customer behavior patterns in areas such as loyalty, creditworthiness, and churn management. This research aims to discover these patterns in the context of electronic banking and using data mining methods. The research data includes more than 6.9 million transactions belonging to about 340,000 Sina Bank customers over an eight-month period. First, using the RFM model, the indicators of novelty, repetition, and monetary value were measured, and then the customers were analyzed with clustering algorithms. The results showed that the K-Means method with three clusters performed best and divided the customers into three groups: "loyal," "at risk," and "potentia". The findings indicate that loyal customers (40%) conduct more than 80% of transactions, while high-risk customers have a small share. Mobile banking was also identified as the most important transaction channel. The results show that data-driven analytics can help improve decision-making, increase loyalty, and reduce customer churn. |
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| Keywords: association rule discovery, customer behavior, customer clustering, Electronic banking, RFM model |
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Full-Text [PDF 1105 kb]
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Type of Study: Research |
Subject:
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