Recent Papers

Credit card fraud has become one of the most prevalent problems in the credit card industry. A basic thought process is to distinguish between different types of credit card counterfeiting and review the optional techniques that have been used to detect fraud. Depending on the different types of credit card fraud faced by financial institutions such as banks and credit card companies, different measures can be takento reduce fraud. The purpose of using these strategies and techniques is to minimize credit card fraud. There are certain unsolved problems with existing techniques that result in some legitimate credit card customers being identified as fraudulent. This white paper focuses on including the best classification algorithms from a set of four different algorithms that are likely to indicate the level of fraud in the financial sector. Data mining (DM) includes core algorithms that enable data beyond basic insight and knowledge. In fact, data mining is part of the knowledge discovery process. A credit card provider (CC) allows customers to use multiple cards. All credit card users must be genuine and honest. Dealing with every mistake can lead to a financial crisis. With cashless transactions growing rapidly, counterfeit transactions are also unlikely to increase. Fraudulent transactions can be identified by looking at credit cards that behave differently than previous transaction history records. Any deviation from the available cost pattern is a bogus trade. DM and machine learning (MLT) techniques are commonly used to detect credit card fraud (CCFD).

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