• Applying Supervised Machine Learning Algorithms for Fraud Detection in Anti-Money Laundering

    Author(s):
    Omri Raiter (see profile)
    Date:
    2021
    Subject(s):
    Machine learning
    Item Type:
    Article
    Tag(s):
    AML, ANN, Logistic Regression, Random Forest, SVM
    Permanent URL:
    http://dx.doi.org/10.17613/2g0z-0814
    Abstract:
    As international money transfers become more automated, it becomes easier for criminals to transfer money across borders in a fraction of a second, while it also becomes easier for regulators to inspect and monitor international money mobility and identify unusual patterns of money movement. Machine learning algorithms may be a useful addition to the current money laundering detection issues. This research empirically tested four machine learning algorithms (Logistic regression, SVM, Random Forest, and ANN) using a synthetic dataset that closely matches regular transaction behavior. After observing the performance of different algorithms, it can be stated that the Random Forest technique, when compared to the other techniques, provides the best accuracy. The least accurate approach was the Artificial Neural Network (ANN).
    Notes:
    How to Cite: Raiter, O. . . (2021). Applying Supervised Machine Learning Algorithms for Fraud Detection in Anti-Money Laundering. Journal of Modern Issues in Business Research, 1(1), 14–26. Retrieved from https://international-journals.website/index.php/JMIB/article/view/4
    Metadata:
    Published as:
    Journal article    
    Status:
    Published
    Last Updated:
    1 year ago
    License:
    Attribution-NonCommercial
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