Fraud Prediction in Movie Theater Credit Card Transactions using Machine Learning
Received: 12 April 2023 | Revised: 29 April 2023 and 1 May 2023 | Accepted: 2 May 2023 | Online: 2 June 2023
Corresponding author: Areej Alshutayri
Abstract
This paper highlights how the proliferation of online transactions, especially those involving the use of credit cards, has resulted in the emergence of new security flaws that pose threats to customers and enterprises worldwide. E-commerce and other forms of online monetary transactions have become essential in the manufacturing and service sectors, propelling the global economy. The widespread and dependent connectivity of mobile payment systems using credit card transactions presents chances for fraud, risk, and security breaches. In light of the importance of accurately predicting fraud incidents through payment procedures, this study investigated the credit card payment methods used for movie tickets, using the machine learning logistic regression method to analyze and predict such incidents. This study used a dataset from cinema ticket credit card transactions made in two days of September 2013 by European cardholders, including 284,807 transactions out of which 492 were fraudulent purchases. The results of the proposed method showed a prediction accuracy of 99%, proving its high prediction performance.
Keywords:
fraud prediction, movie theater, credit card, machine learningDownloads
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