A Survey and a Credit Card Fraud Detection and Prevention Model using the Decision Tree Algorithm

Abstract

Today, many people prefer online payment methods due to the rapid growth in cashless electronic transactions. Credit and debit cards are the most popular electronic payment methods but are prone to fraud due to the nature of their use and the tendency of fraudsters to access their details. This study proposes a theoretical credit fraud detection and prevention model using a Decision Tree Algorithm (DCA). Moreover, a survey questionnaire was used to investigate students' perceptions of credit card fraud incidents. Data were collected from 102 students from different universities and countries around the world. The results showed that 95.9% of the respondents knew how credit/debit card fraud occurs, while 4.1% of them did not. Finally, 81.6% expressed their willingness to use a tool based on the proposed model to prevent or detect credit/debit card fraud incidents.