Abstract
A SURVEY ON MULTIPLE CLASSIFIERS SYSTEM FOR ANOMALY DETECTION IN CREDIT CARD DATA WITH UNBALANCED AND OVERLAPPED CLASSES
*Mukesh Kumar Mandal and Dr. Avinash Sharma
ABSTRACT
Credit card plays a very important rule in today's economy. It becomes an unavoidable part of household, business and global activities. Although using credit cards provides enormous benefits when used carefully and responsibly, significant credit and financial damages may be caused by fraudulent activities. Many techniques have been proposed to confront the growth in credit card fraud. However, all of these techniques have the same goal of avoiding the credit card fraud; each one has its own drawbacks, advantages and characteristics. The widened uses of Internet credit cards in e-banking systems are currently prone to credit card fraud. Data imbalance also poses a significant difficulty in the method of fraud detection. The efficiency of the existing fraud detection systems is only in question because it detects fraudulent action after the suspect transaction has been completed. In this study, a Multiple Classifiers System (MCS) has been used on two data sets: (i) credit card frauds (CCF), and (ii) credit card default payments (CCDP). The MCS employs a sequential decision combination strategy to produce accurate anomaly detection. The empirical studies show that the MCS outperforms the existing research, particularly in detecting the anomalies that are minorities in these two credit card data sets.
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