The term “fraud”, it always concerned about credit card fraud in our minds. And after the significant increase in the transactions of credit card, the fraud of credit card increased extremely in last years. So the fraud detection should include surveillance of the spending attitude for the person/customer to the determination, avoidance, and detection of unwanted behavior. Because the credit card is the most payment predominant way for the online and regular purchasing, the credit card fraud raises highly. The Fraud detection is not only concerned with capturing of the fraudulent practices, but also, discover it as fast as they can, because the fraud costs millions of dollar business loss and it is rising over time, and that affects greatly the worldwide economy. . In this paper we introduce 14 different techniques of how data mining techniques can be successfully combined to obtain a high fraud coverage with a high or low false rate, the Advantage and The Disadvantages of every technique, and The Data Sets used in the researches by researchers
Abd El-Hamid, Hossam Eldin Mohammed Ahmed Abdou; Khalifa, Wael; Roushdy, Mohamed Ismail; and Salem, Abdel-Badeeh M.
"Machine Learning Techniques for Credit Card Fraud Detection,"
Future Computing and Informatics Journal: Vol. 4
, Article 5.
Available at: https://digitalcommons.aaru.edu.jo/fcij/vol4/iss2/5