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Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture

Author

Listed:
  • Esraa Faisal Malik

    (School of Management, Universiti Sains Malaysia, Penang 11800, Malaysia)

  • Khai Wah Khaw

    (School of Management, Universiti Sains Malaysia, Penang 11800, Malaysia)

  • Bahari Belaton

    (School of Computer Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia)

  • Wai Peng Wong

    (School of Information Technology, Monash University, Malaysia Campus, Subang Jaya 47500, Malaysia)

  • XinYing Chew

    (School of Computer Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia)

Abstract

The negative effect of financial crimes on financial institutions has grown dramatically over the years. To detect crimes such as credit card fraud, several single and hybrid machine learning approaches have been used. However, these approaches have significant limitations as no further investigation on different hybrid algorithms for a given dataset were studied. This research proposes and investigates seven hybrid machine learning models to detect fraudulent activities with a real word dataset. The developed hybrid models consisted of two phases, state-of-the-art machine learning algorithms were used first to detect credit card fraud, then, hybrid methods were constructed based on the best single algorithm from the first phase. Our findings indicated that the hybrid model Adaboost + LGBM is the champion model as it displayed the highest performance. Future studies should focus on studying different types of hybridization and algorithms in the credit card domain.

Suggested Citation

  • Esraa Faisal Malik & Khai Wah Khaw & Bahari Belaton & Wai Peng Wong & XinYing Chew, 2022. "Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture," Mathematics, MDPI, vol. 10(9), pages 1-16, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1480-:d:804910
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    References listed on IDEAS

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    1. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
    2. Adrian Gepp & Kuldeep Kumar & Sukanto Bhattacharya, 2021. "Lifting the numbers game: identifying key input variables and a best‐performing model to detect financial statement fraud," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(3), pages 4601-4638, September.
    3. Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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    Cited by:

    1. Ning Han & Wen Xu & Qian Song & Kai Zhao & Yao Xu, 2025. "Application of Interpretable Artificial Intelligence for Sustainable Tax Management in the Manufacturing Industry," Sustainability, MDPI, vol. 17(3), pages 1-17, January.
    2. Mashael Maashi & Bayan Alabduallah & Fadoua Kouki, 2023. "Sustainable Financial Fraud Detection Using Garra Rufa Fish Optimization Algorithm with Ensemble Deep Learning," Sustainability, MDPI, vol. 15(18), pages 1-16, September.
    3. Jay Raval & Pronaya Bhattacharya & Nilesh Kumar Jadav & Sudeep Tanwar & Gulshan Sharma & Pitshou N. Bokoro & Mitwalli Elmorsy & Amr Tolba & Maria Simona Raboaca, 2023. "RaKShA : A Trusted Explainable LSTM Model to Classify Fraud Patterns on Credit Card Transactions," Mathematics, MDPI, vol. 11(8), pages 1-27, April.
    4. Ludivia Hernandez Aros & Luisa Ximena Bustamante Molano & Fernando Gutierrez-Portela & John Johver Moreno Hernandez & Mario Samuel Rodríguez Barrero, 2024. "Financial fraud detection through the application of machine learning techniques: a literature review," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-22, December.

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