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@article{144240,
        author = {Varre Perantalu and BhargavKiran},
        title = {Credit card Fraud Detection using Predictive Modeling: a Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {3},
        number = {9},
        pages = {53-58},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=144240},
        abstract = {In this paper author proposed that fraud detection is a critical problem affecting large financial companies that have increased due to the growth in credit card transactions. This paper presents detection of frauds in credit card transactions, using data mining techniques of Predictive modeling, logistic Regression, and Decision Tree. The data set contains credit card transactions in September 2013 by European cardholders. This data set present transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The data set is highly unbalanced, the positive class(frauds) Account for 0.172% of all transactions.},
        keywords = {Credit card fraud detection ,Data Mining, Predictive modeling, Logistic Regression  , Decision Tree},
        month = {},
        }