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A survey of machine-learning and nature-inspired based credit card fraud detection techniques

Author

Listed:
  • Aderemi O. Adewumi

    (University of Kwa-Zulu Natal)

  • Andronicus A. Akinyelu

    (University of Kwa-Zulu Natal)

Abstract

Credit card is one of the popular modes of payment for electronic transactions in many developed and developing countries. Invention of credit cards has made online transactions seamless, easier, comfortable and convenient. However, it has also provided new fraud opportunities for criminals, and in turn, increased fraud rate. The global impact of credit card fraud is alarming, millions of US dollars have been lost by many companies and individuals. Furthermore, cybercriminals are innovating sophisticated techniques on a regular basis, hence, there is an urgent task to develop improved and dynamic techniques capable of adapting to rapidly evolving fraudulent patterns. Achieving this task is very challenging, primarily due to the dynamic nature of fraud and also due to lack of dataset for researchers. This paper presents a review of improved credit card fraud detection techniques. Precisely, this paper focused on recent Machine Learning based and Nature Inspired based credit card fraud detection techniques proposed in literature. This paper provides a picture of recent trend in credit card fraud detection. Moreover, this review outlines some limitations and contributions of existing credit card fraud detection techniques, it also provides necessary background information for researchers in this domain. Additionally, this review serves as a guide and stepping stone for financial institutions and individuals seeking for new and effective credit card fraud detection techniques.

Suggested Citation

  • Aderemi O. Adewumi & Andronicus A. Akinyelu, 2017. "A survey of machine-learning and nature-inspired based credit card fraud detection techniques," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 937-953, November.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:2:d:10.1007_s13198-016-0551-y
    DOI: 10.1007/s13198-016-0551-y
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    Citations

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    Cited by:

    1. Rui Miguel Dantas & Raheela Firdaus & Farrokh Jaleel & Pedro Neves Mata & Mário Nuno Mata & Gang Li, 2022. "Systemic Acquired Critique of Credit Card Deception Exposure through Machine Learning," JOItmC, MDPI, vol. 8(4), pages 1-20, October.
    2. Rama K. Malladi, 2024. "Application of Supervised Machine Learning Techniques to Forecast the COVID-19 U.S. Recession and Stock Market Crash," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1021-1045, March.
    3. Rosado-Cubero, Ana & Freire-Rubio, Teresa & Hernández, Adolfo, 2022. "Entrepreneurship: What matters most," Journal of Business Research, Elsevier, vol. 144(C), pages 250-263.
    4. Klockmann, Victor & von Schenk, Alicia & Villeval, Marie Claire, 2022. "Artificial intelligence, ethics, and intergenerational responsibility," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 284-317.
    5. Jean Robert Kala Kamdjoug & Hyacinthe Djanan Sando & Jules Raymond Kala & Arielle Ornela Ndassi Teutio & Sunil Tiwari & Samuel Fosso Wamba, 2024. "Data analytics-based auditing: a case study of fraud detection in the banking context," Annals of Operations Research, Springer, vol. 340(2), pages 1161-1188, September.
    6. Tianlang Xiong & Zhishuo Ma & Zhuangzhuang Li & Jiangqianyi Dai, 2022. "The analysis of influence mechanism for internet financial fraud identification and user behavior based on machine learning approaches," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 996-1007, December.
    7. Emanuel Mineda Carneiro & Carlos Henrique Quartucci Forster & Lineu Fernando Stege Mialaret & Luiz Alberto Vieira Dias & Adilson Marques da Cunha, 2022. "High-Cardinality Categorical Attributes and Credit Card Fraud Detection," Mathematics, MDPI, vol. 10(20), pages 1-23, October.
    8. Aslam, Faheem & Hunjra, Ahmed Imran & Ftiti, Zied & Louhichi, Wael & Shams, Tahira, 2022. "Insurance fraud detection: Evidence from artificial intelligence and machine learning," Research in International Business and Finance, Elsevier, vol. 62(C).
    9. K. S. Naik, 2021. "Predicting Credit Risk for Unsecured Lending: A Machine Learning Approach," Papers 2110.02206, arXiv.org.
    10. Bauer, Kevin & Pfeuffer, Nicolas & Abdel-Karim, Benjamin M. & Hinz, Oliver & Kosfeld, Michael, 2020. "The terminator of social welfare? The economic consequences of algorithmic discrimination," SAFE Working Paper Series 287, Leibniz Institute for Financial Research SAFE.

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