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    Review of 'Credit card fraud detection using machine learning: A survey'

    Credit card fraud detection using machine learning: A survey
    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 using machine learning: A survey

    Credit card fraud has emerged as major problem in the electronic payment sector. In this survey, we study data-driven credit card fraud detection particularities and several machine learning methods to address each of its intricate challenges with the goal to identify fraudulent transactions that have been issued illegitimately on behalf of the rightful card owner. In particular, we first characterize a typical credit card detection task: the dataset and its attributes, the metric choice along with some methods to handle such unbalanced datasets. These questions are the entry point of every credit card fraud detection problem. Then we focus on dataset shift (sometimes called concept drift), which refers to the fact that the underlying distribution generating the dataset evolves over times: For example, card holders may change their buying habits over seasons and fraudsters may adapt their strategies. This phenomenon may hinder the usage of machine learning methods for real world datasets such as credit card transactions datasets. Afterwards we highlights different approaches used in order to capture the sequential properties of credit card transactions. These approaches range from feature engineering techniques (transactions aggregations for example) to proper sequence modeling methods such as recurrent neural networks (LSTM) or graphical models (hidden markov models).

      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.

      Review text

      Machine learning models are able to learn from patterns of normal behavior. They are very fast to adapt to changes in that normal behaviour and can quickly identify patterns of fraud transactions. This means that the model can identify suspicious customers even when there hasn't been a chargeback yet.

      The model should be explained in more detail while refering to the valid literature review.

      Overall, explained concept and methodology are properly aligned with the tresearch work.


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