With the rapid development of bank credit card business, banks are facing increasingly serious credit card fraud problems. In order to carry out the credit card business and effectively prevent fraud risks, this paper analyzes the research results at home and abroad, comprehensively considers the availability, security and timeliness of the model. For the unbalanced data set, using machine learning methods, three kinds of anti-balance are proposed. Fraud model. Firstly, data preprocessing is performed by using undersampling method. Secondly, Lasso-Logistic, XGBoost, and the above two models are used to model and predict. Finally, the three methods are compared and optimized. This paper analyzes fraudulent behavior through three kinds of anti-fraud systems, which has positive and important significance for improving the accuracy and effectiveness of credit card fraud detection.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Credit card fraud identification based on unbalanced data set based on fusion model


    Beteiligte:
    Li, Donglin (Autor:in)


    Erscheinungsdatum :

    01.10.2019


    Format / Umfang :

    228891 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Security: New technology could cut web credit card fraud

    British Library Online Contents | 2005


    Comparative Study on GANs and VAEs in Credit Card Fraud Detection

    Sukruth, S / Haripriya, Mc / Deepa, S et al. | IEEE | 2024


    Credit Card Fraud Detection Using Artificial Neural Networks and Random Forest Algorithms

    Pradhan, Sasmita Kumari / Krishna Rao, N V / Deepika, N M et al. | IEEE | 2021


    Expression of Concern for: Credit Card Fraud Detection Using Choice Tree Technology

    Roshan, Aakash / Vyas, Abhilasha / Singh, Upendra | IEEE | 2018

    Freier Zugriff

    Credit Card Fraud Detection Using Local Outlier Factor & Isolation Forest Algorithms: A Complete Analysis

    Ghevariya, Ravi / Desai, Rahul / Bohara, Mohammed Husain et al. | IEEE | 2021