Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture
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- 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.
- 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.
- 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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- 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.
- 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.
- 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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Keywords
classification; credit card; data mining; fraud detection; hybrid; machine learning;All these keywords.
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