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      Credit Card Fraud Detection Techniques: A Survey

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        1 ,
      ScienceOpen Preprints
      Fraud detection, Random forest, Fraudulent behavior detection.


            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.


            Author and article information

            ScienceOpen Preprints
            6 July 2022
            [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

            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


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