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

Aghware ; Fidelis Obukohwo ; Ojugo ; Arnold Adimabua ; Adigwe ; Wilfred ; Odiakaose ; Christopher Chukwufumaya ; Ojei ; Emma Obiajulu ; Ashioba ; Nwanze Chukwudi ; Okpor ; Margareth Dumebi ; Geteloma ; Victor Ochuko
Journal of Computing Theories and Applications 📅 2024 Vol 1, No 4 🔗 DOI: 10.62411/jcta.10323
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.
Keywords
Credit card fraud detection; Feature selection; Imbalanced dataset; Random Forest; SMOTE
How to Cite

Aghware, et al. (2024). Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection. Journal of Computing Theories and Applications, 1(4). https://doi.org/10.62411/jcta.10323

Aghware, Fidelis Obukohwo; Ojugo, Arnold Adimabua; Adigwe, Wilfred; Odiakaose, Christopher Chukwufumaya; Ojei, Emma Obiajulu; Ashioba, Nwanze Chukwudi; Okpor, Margareth Dumebi; Geteloma, Victor Ochuko, "Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection," Journal of Computing Theories and Applications, vol. 1, no. 4, 2024.

Aghware, Fidelis Obukohwo; Ojugo, Arnold Adimabua; Adigwe, Wilfred; Odiakaose, Christopher Chukwufumaya; Ojei, Emma Obiajulu; Ashioba, Nwanze Chukwudi; Okpor, Margareth Dumebi; Geteloma, Victor Ochuko. "Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection." Journal of Computing Theories and Applications, vol. 1, no. 4, 2024.

Aghware, Fidelis Obukohwo; Ojugo, Arnold Adimabua; Adigwe, Wilfred; Odiakaose, Christopher Chukwufumaya; Ojei, Emma Obiajulu; Ashioba, Nwanze Chukwudi; Okpor, Margareth Dumebi; Geteloma, Victor Ochuko. "Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection." Journal of Computing Theories and Applications 1, no. 4 (2024).

Aghware, et al. (2024) 'Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection', Journal of Computing Theories and Applications, 1(4). doi: 10.62411/jcta.10323.

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

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