DIFFERENT TECHNIQUES USED FOR CREDIT CARD FRAUD DETECTION – A LITERATURE SURVEY

Computer Engineering

Credit cards are the most used type of electronic payment because of the rise in everyday electronic transactions, which increases their vulnerability to fraud. The cost of card fraud has been high for credit card companies. Currently, finding credit card fraud is the most common issue. The correct procedures & technology are being sought for by credit card firms to prevent & detect fraudulent credit card transactions. For clients to avoid being charged for products they didn’t buy, credit card issuers must be able to recognize fraudulent credit card transactions. Data Science may be used to solve these issues, & coupled with machine learning; it is of utmost relevance. Modelling previous credit card transactions using information from those that turned out to be fraudulent is part of the Credit Card Fraud Detection Problem. The validity of a new transaction is then determined using this approach. A classic example of classification is the detection of credit card fraud. Fraudulent actions are often always meant to harm the second party financially. The number of fraudulent actions will rise even further as digital money becomes more widespread in different nations. Every year, these fraudulent activities cost banks & credit card firms billions of dollars in lost income & have a negative impact on many employees' careers. More information on the various credit card fraud schemes is covered in the planned study project. The technical & review publications on credit card fraud detection that have been published in recent years are categorized, compared, & summarized in this survey report.

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