Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach

Abdul Rehman Khalid, Nsikak Owoh, Omair Uthmani, Moses Ashawa, Jude Osamor and John Adejoh 2024. Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach. Big Data and Cognitive Computing. 8 (1) 6. https://doi.org/10.3390/bdcc8010006

TitleEnhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach
TypeJournal article
AuthorsAbdul Rehman Khalid, Nsikak Owoh, Omair Uthmani, Moses Ashawa, Jude Osamor and John Adejoh
Abstract

In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as credit card fraud techniques continue to evolve.

Article number6
JournalBig Data and Cognitive Computing
Journal citation8 (1)
ISSN2504-2289
Year2024
PublisherMDPI
Publisher's version
License
CC BY 4.0
File Access Level
Open (open metadata and files)
Digital Object Identifier (DOI)https://doi.org/10.3390/bdcc8010006
Web address (URL)https://doi.org/10.3390/bdcc8010006
Publication dates
Published03 Jan 2024

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