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      Credit Card Fraud Detection Techniques: A Survey

      Preprint
      In review
      research-article
        1 ,
      ScienceOpen Preprints
      ScienceOpen
      Fraud detection, Random forest, Fraudulent behavior detection.
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            Abstract

            Fraud events are increasing on day to day bases. Credit card fraud is the most frequent fraud that is making a big financial loss on a global level. Researchers have implemented many machine learning algorithms to detect the credit card frauds. This research has briefly described several algorithms and compares the performance of Random Forest, Naïve Bayes, K-Nearest Neighbor, Logistic Regression and Multilayer Perceptron. Algorithms are also used to classify the real transactions or fraudulent transactions. These datasets are compared on the basis of accuracy, precision, recall & false positive rate. Comparison results show that Random Forest performs best in credit card fraud detection dataset among others. Research shows that any ML algorithm can be used to demonstrate the classification of fraud detection.

            Content

            Author and article information

            Journal
            ScienceOpen Preprints
            ScienceOpen
            6 July 2022
            Affiliations
            [1 ] SST, University of Management and Technology, University of Management & Technology (UMT) C-II Block C 2 Phase 1 Johar Town, Lahore, Punjab 54770, Pakistan
            Author notes
            Article
            10.14293/S2199-1006.1.SOR-.PPFI7P0.v1
            80a0f97f-c092-4e0c-a37a-834e1e91adfb

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .


            The datasets generated during and/or analysed during the current study are available in the repository: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
            Security & Cryptology
            Fraud detection,Random forest,Fraudulent behavior detection,Security

            References

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            2. Awoyemi John O., Adetunmbi Adebayo O., Oluwadare Samuel A.. Credit card fraud detection using machine learning techniques: A comparative analysis. 2017 International Conference on Computing Networking and Informatics (ICCNI). 2017. IEEE. [Cross Ref]

            3. Xuan Shiyang, Liu Guanjun, Li Zhenchuan, Zheng Lutao, Wang Shuo, Jiang Changjun. Random forest for credit card fraud detection. 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC). 2018. IEEE. [Cross Ref]

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            7. Kundu Amlan, Sural Shamik, Majumdar A. K.. Two-Stage Credit Card Fraud Detection Using Sequence AlignmentInformation Systems Security. p. 260–275. 2006. Springer Berlin Heidelberg. [Cross Ref]

            8. Mishra Ankit, Ghorpade Chaitanya. Credit Card Fraud Detection on the Skewed Data Using Various Classification and Ensemble Techniques. 2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). 2018. IEEE. [Cross Ref]

            9. Kulkarni Ajay, Chong Deri, Batarseh Feras A.. Foundations of data imbalance and solutions for a data democracyData Democracy. p. 83–106. 2020. Elsevier. [Cross Ref]

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