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Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection

Aghware, Fidelis Obukohwo and Ojugo, Arnold Adimabua and Adigwe, Wilfred and Odiakaose, Christopher Chukwufumaya and Ojei, Emma Obiajulu and Ashioba, Nwanze Chukwudi and Okpor, Margareth Dumebi and Geteloma, Victor Ochuko (2024) Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection. Journal of Computing Theories and Applications, 1 (4). pp. 407-420. ISSN 3024-9104

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Abstract

Fraudsters increasingly exploit unauthorized credit card information for financial gain, targeting un-suspecting users, especially as financial institutions expand their services to semi-urban and rural areas. This, in turn, has continued to ripple across society, causing huge financial losses and lowering user trust implications for all cardholders. Thus, banks cum financial institutions are today poised to implement fraud detection schemes. Five algorithms were trained with and without the application of the Synthetic Minority Over-sampling Technique (SMOTE) to assess their performance. These algorithms included Random Forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machines (SVM), and Logistic Regression (LR). The methodology was implemented and tested through an API using Flask and Streamlit in Python. Before applying SMOTE, the RF classifier outperformed the others with an accuracy of 0.9802, while the accuracies for LR, KNN, NB, and SVM were 0.9219, 0.9435, 0.9508, and 0.9008, respectively. Conversely, after the application of SMOTE, RF achieved a prediction accuracy of 0.9919, whereas LR, KNN, NB, and SVM attained accuracies of 0.9805, 0.9210, 0.9125, and 0.8145, respectively. These results highlight the effectiveness of combining RF with SMOTE to enhance prediction accuracy in credit card fraud detection.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Depositing User: dl fts
Date Deposited: 24 Nov 2024 07:29
Last Modified: 24 Nov 2024 07:57
URI: https://dl.futuretechsci.org/id/eprint/33

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