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@article{157570,
        author = {Sagar Yadav and Vijay Gaikwad and Suvarna Mane and Disha Chandak and Om Pardeshi and Tanaya Barawkar and  Gopal Dhanpalwar},
        title = { Random Forest Classifier for Credit Card Fraud Detection  },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {7},
        pages = {587-593},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=157570},
        abstract = {Credit card fraud (CCF) is a straightforward and appealing target. Online sites as well as E- commerce have enlarged their payment options, as risk of online fraud is increasing rapidly. Researchers began using various machine learning (ML) algorithms to detect and analyses online transaction fraud as fraud rates increased. This paper presents a random forest-based model to detect fraudulent transactions by analyzing customers' historical transaction details and extracting behavioral patterns. Cardholders are divided into groups based on the volume of their transactions. Then, using a sliding window method, the transactions performed by cardholders are aggregated from various categories.  The behavioral patterns of the various groupings are then derived. The random forest (RF) classifier shows the greatest accuracy and hence proved to be one of the most excellent ways for detection/prediction of frauds. As a result, a feedback mechanism is implemented to address the issue of notion drift. The proposed model provides high accuracy of 99.99% with precision of 93% and recall of 73%. The proposed model provides better performance than isolation forest algorithm as well logistic regression and support vector machine.},
        keywords = {fraud detection, random forest algorithm, machine learning, credit card fraud detection, isolation forest algorithm.},
        month = {},
        }