Ensemble Learning with Supervised Machine Learning Models to Predict Credit Card Fraud Transactions

• Published in: Revue d intelligence artificielle Vol. 36; no. 4; p. 509
• Main Authors: Mohammed Rashad Baker, Mahmood, Zuhair Norii, Ehab Hashim Shaker
• Format: Journal Article
• Language: English
• Published: Edmonton International Information and Engineering Technology Association (IIETA) 01.08.2022
• Subjects: Accuracy; Algorithms; Credit card fraud; Datasets; Deep learning; Energy consumption; Ensemble learning; Fraud prevention; Literature reviews; Machine learning; Privacy; Supervised learning
• ISSN: 0992-499X; 1958-5748; 1958-5748
• DOI: 10.18280/ria.360401

In recent years, the highly boosting development in e-commerce technologies made it possible for people to select the most desirable items from shops and stores worldwide while being at home. Credit card frauds transactions are common nowadays because of online payments. Online transactions are the root cause of fraudulent credit card activity, bringing enormous financial losses. Financial institutions must install an automatic deterrent mechanism to check these fraudulent actions. The fraudulent transactions do not follow a specific pattern and continuously change their shape and behavior. This paper aims to use ensemble learning with supervised Machine Learning (ML) models to predict the occurrence of fraud transactions. The experimental study has been evaluated on the open-source Kaggle credit card fraud detection dataset. The performance of the proposed model is measured in terms of accuracy score, confusion matrix, and classification report. The results were state-of-the-art using the voting ensemble learning technique shows that it can be get the best results using PCA with 100.0% accuracy, 97.3% precision, 73.5% recall, and 83.7% f1-score against other ML classifiers.