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

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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.

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Authors and Affiliations

  1. School of Mathematics, Statistics and Computer Science, University of Kwa-Zulu Natal, University Road, Westville, Private Bag X 54001, Durban, 4000, South Africa

    Aderemi O. Adewumi & Andronicus A. Akinyelu

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  1. Aderemi O. Adewumi
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  2. Andronicus A. Akinyelu
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Correspondence to Aderemi O. Adewumi.

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Adewumi, A.O., Akinyelu, A.A. A survey of machine-learning and nature-inspired based credit card fraud detection techniques. Int J Syst Assur Eng Manag 8 (Suppl 2), 937–953 (2017). https://doi.org/10.1007/s13198-016-0551-y

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  • DOI: https://doi.org/10.1007/s13198-016-0551-y

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