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@article{145940,
        author = {J. Gayathri and Mylapoore Madhu},
        title = {Credit Card Fraud Detection System using Visual   Analytics through the Data Mining},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {11},
        pages = {788-793},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=145940},
        abstract = {Financial institutions are interested in ensuring security and quality for their customers. Banks, for instance, need to identify and stop harmful transactions in a timely manner. In order to detect fraudulent operations, data mining techniques and customer profile analysis are commonly used. Thus, we propose EVA, a Visual Analytics approach for supporting fraud investigation, fine-tuning fraud detection algorithms, and thus, reducing false positive alarms. Due to the theatrical increases of fraud which results in loss of dollars worldwide each year, several modern techniques in detecting fraud are persistently evolved and applied to many business fields. The goal of this paper is to provide a security in credit card transaction using EVA technique to detect fraud. The credit card fraud detection features uses user behavior and location scanning to check for unusual patterns.},
        keywords = {Visual Knowledge Discovery, Data Mining, Financial Visualization, Financial Fraud Detection. },
        month = {},
        }