Artificial Intelligence plays a key role in detecting credit card frauds. Combined with shared experience database, the power of the artificial intelligence approach, makes the system smarter with each new credit card transaction. Neural networks are used to analyze the data elements of a transaction and determine whether or not the transaction is potentially fraudulent. The paper suggests certain preventive measures to combat credit card fraud.

During the recent years, there has been a global increase in online fraud, namely, credit card frauds, which has caused huge losses to online business. According to the 2005 CyberSource Online Fraud Report, credit card fraud had cost its merchants nearly $2.6 bn, or 1.8%, of the total annual revenues. In spite of the existing prevention technologies to reduce fraud, the fraudsters mange to find ways to evade such measures. Fraud Detection, which is a continuously evolving discipline, needs a smart and effective tool to get accustomed to fraudsters' strategies and their tactics. One such effective and efficient technique to detect credit card frauds is Artificial Intelligence (AI) via Neural Networks Software systems.

AI plays a key role in detecting fraudulent activities in credit card transactions. The power of an artificial intelligence approach, when combined with shared experience database, makes the system grow smarter with each new transaction. One of the ways to make a fraud detection system more AI friendly is to capture more information in a transaction. The data captured in the transaction will be used to generate a more comprehensive profile of a cardholder. There are various ways through which credit card fraud can be perpetrated, such as simple theft, application fraud and counterfeit fraud. In all these instances, a fraudster uses a physical card but the major fraud area is, "cardholder-not present fraud", in which a transaction is made by entering only the card details.

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