Credit Card Fraud Detection using FPGA with K-NN Clustering

Credit Card Fraud is one of the biggest threats to business establishments today. However, to combat the fraud effectively, it is important to first understand the mechanisms of executing a fraud. Credit card fraudsters employ a large number of modus operandi to commit fraud. In simple terms, Credit Card Fraud is defined as: "When an individual uses another individuals credit card for personal reasons while the owner of the card and the card issuer are not aware of the fact that the card is being used. Further, the individual using the card has no connection with the cardholder or issuer, and has no intention of either contacting the owner of the card or making repayments for the purchases made." Credit card frauds are committed in the following ways: An act of criminal deception (mislead with intent) by use of unauthorized account and/or personal information - Illegal or unauthorized use of account for personal gain - Misrepresentation of account information to obtain goods and/or services. Contrary to popular belief, merchants are far more at risk from credit card fraud than the cardholders. While consumers may face trouble trying to get a fraudulent charge reversed, merchants lose the cost of the product sold, pay chargeback fees, and fear from the risk of having their merchant account closed. Increasingly, the card not present scenario, such as shopping on the internet poses a greater threat as the merchant (the web site) is no longer protected with advantages of physical verification such as signature check, photo identification, etc. In fact, it is almost impossible to perform any of the physical world checks necessary to detect who is at the other end of the transaction. This makes the internet extremely attractive to fraud perpetrators. According to a recent survey, the rate at which internet fraud occurs is 12 to 15 times higher than physical world fraud. However, recent technical developments are showing some promise to check fraud in the card not present scenario.