Year: 2021
Type: article
Abstract: Data Mining (DM) plays an important role in banking sector to help improve customer experience especially when using automated teller machine (ATM). The role of ATM has been maximized recently due to the national trend of digital transformation and the emergence of COVID-19 pandemic since the ATM provides banks’ customers with financial transaction method in public places without any human interaction. which led to the difficulty of financial institutions in measuring the performance of automated teller machines and counting data Whether it is data with a direct or indirect impact on the machines This paper presents a model that helps measuring the efficiency of the ATM by building data warehouse (DW) (Schedule server of ATM, Location, transaction and customer relationship management (CRM)and compares between two DM algorithms (decision tree and naive baye). The decision tree (C5) has been proved to be more convenient method for measuring an ATM's efficiency.
Authors: Abeer Amer, Mohamed Ebied, Nevine Labib
Institution: Sadat Academy for Management Sciences
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4389856487