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Credit Card Fraud Detection: Addressing Imbalanced Datasets with a Multi-phase Approach

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Abstract

Credit card fraud detection plays a crucial role in safeguarding the financial security of individuals and organizations. However, imbalanced datasets pose significant challenges to accurately identifying fraudulent transactions. In this research paper, we propose a novel approach that combines autoencoder (AE) and fully connected deep networks (FCDN) models to address this issue. The process involves three phases: training an AE on fraudulent transactions, utilizing another AE for dimensionality reduction, and using the encoded representations as input for FCDN classification. To further enhance the model’s performance, we introduce an additional FCDN trained on the preprocessed data using the synthetic minority oversampling technique (SMOTE). The predictions from both AE, AE–FCDN, and the FCDN are combined using a majority voting approach. We evaluate the proposed method using standard performance metrics, including accuracy, precision, recall, and F1-score. Our experimental results demonstrate the effectiveness and robustness of the integrated model architecture in accurately detecting credit card fraud. These findings provide valuable insights for improving financial security measures and mitigating potential losses associated with credit card fraud.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Authors and Affiliations

  1. LISAC Laboratory, Department of Computer Science, Faculty of Sciences, University Sidi Mohammed Ben Abdellah Atlas, 30050, Fes, Morocco

    Fatima Zohra El Hlouli, Jamal Riffi, Mohamed Adnane Mahraz, Ali Yahyaouy, Khalid El Fazazy & Hamid Tairi

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  1. Fatima Zohra El Hlouli
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  2. Jamal Riffi
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  3. Mohamed Adnane Mahraz
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  4. Ali Yahyaouy
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  5. Khalid El Fazazy
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  6. Hamid Tairi
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Contributions

FZEH played a key role in conceptualizing, developing, and analyzing the idea, as well as making significant contributions to the manuscript. Collaborating closely, JR refined the idea, implemented algorithms, analyzed results, and provided valuable input to the manuscript. MAM actively participated in study design, algorithm implementation, result analysis, and contributed to the writing and revision process. AY made important contributions to result analysis, interpretation, and manuscript writing and revision. KEF extensively contributed to study design, algorithm implementation, result analysis, and manuscript writing and revision. Hamid Tairi assisted with algorithm implementation, result analysis, and contributed to the manuscript. Together, their collaborative efforts greatly enhanced the study and manuscript.

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Correspondence to Fatima Zohra El Hlouli.

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El Hlouli, F.Z., Riffi, J., Mahraz, M.A. et al. Credit Card Fraud Detection: Addressing Imbalanced Datasets with a Multi-phase Approach. SN COMPUT. SCI. 5, 173 (2024). https://doi.org/10.1007/s42979-023-02559-6

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