IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i9p1480-d804910.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/9/1480/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/9/1480/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiaowei Chen & Cong Zhai, 2023. "Bagging or boosting? Empirical evidence from financial statement fraud detection," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(5), pages 5093-5142, December.
    2. Wosnitza, Jan Henrik, 2022. "Calibration alternatives to logistic regression and their potential for transferring the dispersion of discriminatory power into uncertainties of probabilities of default," Discussion Papers 04/2022, Deutsche Bundesbank.
    3. Lucian Belascu & Alexandra Horobet & Georgiana Vrinceanu & Consuela Popescu, 2021. "Performance Dissimilarities in European Union Manufacturing: The Effect of Ownership and Technological Intensity," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    4. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    5. Tobias Götze & Marc Gürtler & Eileen Witowski, 2020. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 21(5), pages 428-446, September.
    6. Roy Cerqueti & Francesca Pampurini & Annagiulia Pezzola & Anna Grazia Quaranta, 2022. "Dangerous liasons and hot customers for banks," Review of Quantitative Finance and Accounting, Springer, vol. 59(1), pages 65-89, July.
    7. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis, 2019. "Forecasting transportation demand for the U.S. market," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 195-214.
    8. Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021. "Applications of machine learning for corporate bond yield spread forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    9. Ortiz-Barrios, Miguel & Arias-Fonseca, Sebastián & Ishizaka, Alessio & Barbati, Maria & Avendaño-Collante, Betty & Navarro-Jiménez, Eduardo, 2023. "Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study," Journal of Business Research, Elsevier, vol. 160(C).
    10. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, April.
    11. Fraisse, Henri & Laporte, Matthias, 2022. "Return on investment on artificial intelligence: The case of bank capital requirement," Journal of Banking & Finance, Elsevier, vol. 138(C).
    12. Vasilios Plakandaras & Elie Bouri & Rangan Gupta, 2019. "Forecasting Bitcoin Returns: Is there a Role for the U.S. – China Trade War?," Working Papers 201980, University of Pretoria, Department of Economics.
    13. Yiyan Huang & Cheuk Hang Leung & Xing Yan & Qi Wu & Nanbo Peng & Dongdong Wang & Zhixiang Huang, 2020. "The Causal Learning of Retail Delinquency," Papers 2012.09448, arXiv.org.
    14. Steven Heston & Nitish R. Sinha, 2016. "News versus Sentiment : Predicting Stock Returns from News Stories," Finance and Economics Discussion Series 2016-048, Board of Governors of the Federal Reserve System (U.S.).
    15. Berge, Travis J., 2018. "Understanding survey-based inflation expectations," International Journal of Forecasting, Elsevier, vol. 34(4), pages 788-801.
    16. Flood, Mark D. & Lemieux, Victoria L. & Varga, Margaret & William Wong, B.L., 2016. "The application of visual analytics to financial stability monitoring," Journal of Financial Stability, Elsevier, vol. 27(C), pages 180-197.
    17. Cristian KEVORCHIAN & Camelia GAVRILESCU & Gheorghe HURDUZEU, 2015. "An Approach Based On Big Data And Machine Learning For Optimizing The Management Of Agricultural Production Risks," Agricultural Economics and Rural Development, Institute of Agricultural Economics, vol. 12(2), pages 117-128.
    18. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    19. Marcel Lucas Chee & Marcus Eng Hock Ong & Fahad Javaid Siddiqui & Zhongheng Zhang & Shir Lynn Lim & Andrew Fu Wah Ho & Nan Liu, 2021. "Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review," IJERPH, MDPI, vol. 18(9), pages 1-15, April.
    20. Hinterlang, Natascha & Hollmayr, Josef, 2022. "Classification of monetary and fiscal dominance regimes using machine learning techniques," Journal of Macroeconomics, Elsevier, vol. 74(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1480-:d:804910. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.