+1 Recommend
    • Review: found
    Is Open Access

    Review of 'Credit Card Fraud Detection Techniques: A Survey'

    Credit Card Fraud Detection Techniques: A SurveyCrossref
    Can be considered for publication
    Average rating:
        Rated 4 of 5.
    Level of importance:
        Rated 4 of 5.
    Level of validity:
        Rated 3 of 5.
    Level of completeness:
        Rated 4 of 5.
    Level of comprehensibility:
        Rated 4 of 5.
    Competing interests:

    Reviewed article

    • Record: found
    • Abstract: found
    • Article: found
    Is Open Access

    Credit Card Fraud Detection Techniques: A Survey

    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.

      Review information

      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.

      Security & Cryptology
      Security,Fraudulent behavior detection,Random forest,Fraud detection
      ScienceOpen disciplines:

      Review text

      The paper is very short, some more points can can be included to authenticate the results and more references must be taken.


      Thanks for the review. I'll have a look regarding your suggestion.

      2022-10-07 19:01 UTC

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