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      A Review: Credit Card Fraud Detection in Banks using Machine Learning Algorithms

      Preprint
      In review
      research-article
        1 ,
      ScienceOpen Preprints
      ScienceOpen
      Fraud detection, Random forest, Fraudulent behavior detection., Machine Learning
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            Revision notes

            Some changes in the introduction is made. Some of the information is updated and format of the paper is shifted to two column instead of one column.

            Abstract

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            Author and article information

            Journal
            ScienceOpen Preprints
            ScienceOpen
            6 February 2023
            Affiliations
            [1 ] ;
            Author notes
            Author information
            https://orcid.org/0000-0003-3869-8558
            Article
            10.14293/S2199-1006.1.SOR-.PPFI7P0.v2
            843e1834-bea0-48d6-99fd-a11153ae4182

            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 .

            History
            : 6 July 2022
            Categories

            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.,Machine Learning

            References

            1. Abdallah Aisha, Maarof Mohd Aizaini, Zainal Anazida. Fraud detection system: A survey. Journal of Network and Computer Applications. Vol. 68:90–113. 2016. Elsevier BV. [Cross Ref]

            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]

            4. Varmedja Dejan, Karanovic Mirjana, Sladojevic Srdjan, Arsenovic Marko, Anderla Andras. Credit Card Fraud Detection - Machine Learning methods. 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH). 2019. IEEE. [Cross Ref]

            5. Bagga Siddhant, Goyal Anish, Gupta Namita, Goyal Arvind. Credit Card Fraud Detection using Pipeling and Ensemble Learning. Procedia Computer Science. Vol. 173:104–112. 2020. Elsevier BV. [Cross Ref]

            6. Saleh Hussein Ameer, Salah Khairy Rihab, Mohamed Najeeb Shaima Miqdad, Alrikabi Haider Th.Salim. Credit Card Fraud Detection Using Fuzzy Rough Nearest Neighbor and Sequential Minimal Optimization with Logistic Regression. International Journal of Interactive Mobile Technologies (iJIM). Vol. 15(05)2021. International Association of Online Engineering (IAOE). [Cross Ref]

            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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