USING DATA ANALYTICS AND DATA MINING METHODS TO DETERMINE A HIGH NET WORTH INDIVIDUAL’S ELECTRONIC BANKING BEHAVIOR

Jeanine Schutte (), Alta Van Der Merwe and Fransonet Reyneke
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Jeanine Schutte: Department of Informatics, University of Pretoria, South Africa
Alta Van Der Merwe: Department of Informatics, University of Pretoria, South Africa
Fransonet Reyneke: Department of Statistics, University of Pretoria, South Africa

Journal of Internet Banking and Commerce, 2017, vol. 22, issue 03, 01-39

Abstract: Electronic banking is becoming more popular every day. Financial institutions have accepted the transformation to provide electronic banking facilities to their customers in order to remain relevant and thrive in an environment that is competitive. A contributing factor to the customer retention rate is the frequent use of multiple online functionality however despite all the benefits of electronic banking, some are still hesitant to use it because of security concerns. The perception is that gender, age, education level, salary, culture and profession all have an impact on electronic banking usage. This study reports on how the Knowledge Discovery and Data Mining (KDDM) process was used to determine characteristics and electronic banking behavior of high net worth individuals at a South African bank. Findings indicate that product range and age had the biggest impact on electronic banking behavior. The value of user segmentation is that the financial institution can provide a more accurate service to their users based on their preferences and online banking behavior.

Keywords: Electronic Banking; Behavior; Data Analytics; Data Mining; KDDM (search for similar items in EconPapers)
JEL-codes: A11 (search for similar items in EconPapers)
Date: 2017
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