CHAPTER 3 OBJECTIVE. received a lot of attention on the research front, in particular from payment card

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1 OBJECTIVE 87

2 CHAPTER 3 OBJECTIVE In recent years, topics such as fraud detection and fraud prevention have received a lot of attention on the research front, in particular from payment card issuers. The reason for this increase in research activity can be attributed to the huge annual financial losses incurred by card issuers due to fraudulent use of their card products. A successful strategy for dealing with fraud can quite literally mean millions of dollars in savings per year on operational costs. Fraud prevention is interesting for financial institutions. The advent of new technologies as telephone, Automated Teller Machines (ATMs) and credit card systems have amplified the amount of fraud loss for many banks. Performing the analysis manually is literally impossible, while automation of this process might present a lot of practical difficulties. Analyzing every transaction is legitimate or not is very expensive. Moreover it is also time consuming, hence it is not practically possible. Confirming whether a transaction was done by a client or a fraudster is a better option, but by phoning all card holders is cost prohibitive if it is check in all transactions. Further it might also lead to customer dissatisfaction. Fraud prevention by automatic fraud detections is where the well-known classification methods can be applied, where pattern 88

3 recognition systems play a very important role. One can learn from past (fraud happened in the past) and classify new instances (transactions). Past data about the customer is available in huge amounts, which can be mined for useful data. This old data can be analyzed and the buying behavior of the user can be obtained. This pattern can be used for comparing with the current transactions and determining the legitimacy of the transaction. Fraud detection model is among the most complicated models used for the credit card industry. Skewness of the data, search space dimensionality, different cost of false positive and false negative, durability of the model and short time-to-answer are among the problems one has to face in developing a fraud detection model. Billions of dollars are lost annually due to credit card fraud.the 10th annual online fraud report by cyber source shows that although the percentage loss of revenues has been a steady 1.4% of online payments for the last three years (2006 to 2008), the actual amount has gone up due to growth in online sales. The estimated loss due to online fraud is $4 billion for 2008, an increase of 11% on the 2007 loss of $3.6 billion. With the growth in credit card transactions, as a share of the payment system, there has also been an increase in credit card fraud, and 70% of U.S. consumers are noted to be significantly concerned about identity fraud. Additionally, credit card fraud has broader ramifications, as such fraud helps fund organized crime, international narcotics trafficking, and even terrorist financing. Over the years, along with the evolution of fraud detection methods, perpetrators of fraud have also been evolving their fraud practices to avoid detection. Therefore, credit card fraud detection methods need constant innovation. 89

4 Boltan and Hand [76] note a dearth of published literature on credit card fraud detection, which makes exchange of ideas difficult and holds back potential innovation in fraud detection. Not many researches are provided with the original transaction data for testing the accuracy of their work. Further, not many developed techniques for fraud detection are discussed in public. This is due to the fact that the fraudsters might get access to this methodology, which might prove to be counterproductive. A good discussion on the issues and challenges in fraud detection research is provided in and Credit card transaction databases usually have a mix of numerical and categorical attributes. Transaction amount is the typical numerical attribute, and categorical attributes are those like merchant code, merchant name, date of transaction etc. Some of these categorical variables can, depending on the dataset, have hundreds and thousands of categories. This mix of few numerical and large categorical attributes have spawned the use of a variety of statistical, machine learning, and data mining tools Another issue is that the value of fraud detection is a function of time. The quicker a fraud gets detected, the greater the avoidable loss. However, most fraud detection techniques need history of card holders' behavior for estimating models. Past research suggests that fraudsters try to maximize spending within short periods before frauds get detected and cards are withdrawn. Keeping this issue in mind, this work created derived attributes by aggregating transactions over different time periods to help capture change in spending behavior. Detecting fraud is a challenging task as fraud coexists with the latest in technology. The problem to detect the fraud is that the dataset is unbalanced where non-fraudulent class heavily dominates the fraudulent class. 90

5 The necessity for a credit card system could be avoided if the user reports missing cards or anomalies in the transaction. But according to information from credit card incidents and control measures it is given clearly with proof that it is not possible for users to detect all types of credit card frauds. Though physical card theft could be reported, it is just very meager and does not contribute to the overall loss due to fraud. The actual loss is because of highly organized theft of card credentials and its use for funding illegal activities. So human or users could not be considered as an effective mechanism for detection or prevention of credit card fraud. Hence these works go in for an automated system for fraud detection. The control system currently in place requires a lot of human work involving the analyst to make calls to the customers or block the transaction that is identified as a fraudulent one by the system. This approach may not be welcome by the customers of the banking system as they may have to undergo several verifications even if it a genuine transaction. An automated approach would prove to be much more efficient in detecting fraudulent transactions. It can also help in faster detection of fraudulent transactions. The faster a fraud is detected, the easier it becomes for the user to block the card and prevent further loss. This further helps in the detection of the criminal performing the act. Hence efficient automation of the transaction processing system along with the fraud prediction system is necessary. There also exist many pitfalls, while performing this process. The labeling of a legitimate transaction as fraud will prove to be costly. Hence the predictability of the system should be monitored in such a way that the false positive rates of the system should be eliminated, or at the least case be kept minimum. Here various methodologies are used that help in faster and accurate detection of fraudulent transactions. A hybrid approach could enhance the predicting 91

6 ability of the system thereby minimizing the analysis and the time required for processing the fraudulent transactions. 3.1 Summary The objective of the current work is discussed in this section. It presents a brief overview of all the problems that are being faced due to the frauds, the intensity of the frauds that are occurring in the current electronic world and the pushing necessity for the development of an accurate fraud detection system. Further, it also discusses the problems faced by the researches due to the unavailability of appropriate data, and the reason for banks not willingly providing the original data. 92