Design And Implementation Of A Credit Card Fraud Detection System

1.1 BACKGROUND OF THE STUDY 

In our day to day life, online transactions are increased to purchase goods and services. According to Nielsen study conducted in 2007-2008, 28 of the world’s total population has been using internet. 85 of these people has used internet to make online shopping and the rate of making online purchasing has increased by 40 from 2005 to 2008. The most common method of payment for online purchase is credit card .In developed countries and also in developing countries to some extent ,credit card is most acceptable payment mode for online and offline transaction. It can give financial loses to issuing authorities .In the second method purchasing i.e.

Online, these transactions generally happen on telephone or internet and to make this kind of transaction, the user will need some important information about a credit card (such as credit card number, validity, CVV number, name of card holder).

In our daily lives, credit cards are used for purchasing goods and services with the help of virtual card for online transaction or physical card for offline transaction. In a physical-card based purchase, the cardholder presents his card physically to a merchant for making a payment. To carry out fraudulent transactions in this kind of purchase, an attacker has to steal the credit card. If the cardholder does not realize the loss of card, it can lead to a substantial financial loss to the credit card company. In online payment mode, attackers need only little information for doing fraudulent transaction (secure code, card number, expiration date etc.). In this purchase method, mainly transactions will be done through Internet or telephone. To commit fraud in these types of purchases, a fraudster simply needs to know the card details. Most of the time, the genuine cardholder is not aware that someone else has seen or stolen his card information. The only way to detect this kind of fraud is to analyze the spending patterns on every card and to figure out any inconsistency with respect to the “usual” spending patterns. Fraud detection based on the analysis of existing purchase data of cardholder is a promising way to reduce the rate of successful credit card frauds. Since humans tend to exhibit specific behaviorist profiles, every Card holder can be represented by a set of patterns containing information about the typical purchase category, the time since the last purchase, the amount of money spent, etc.

Deviation from such patterns is a potential threat to the system