CREDIT CARD FRAUD DETECTION BASED ON MACHINE LEARNING

Machine learning

Any activity taken with the intention of stealing money from another person is referred to as fraud. Now that it is more widely accepted, digital cash is used more frequently. Each year, these illegal operations cost banks and credit card companies billions of dollars in revenue and endanger the careers of several workers.Credit card fraud has considerably increased during the past few years. Credit card users, companies that accept them, institutions, and shops have all suffered huge financial losses. Machine learning is one of the greatest techniques to identify fraud.In this study, various machine learning fraud detection techniques are contrasted and compared using Specificity, accuracy, and precision are examples of performance criteria. Additionally, the research advises employing the Rough Forest strategy and supervised FDS.The suggested approach improves the ability as a way to spot credit card fraud. Additionally, the proposed system successfully solves the issue of idea drift in fraud prevention by using a method of ranking the alert using learning to rank. The purpose of this programme is to spot fraud involving credit cards in dubious transactions.Using machine learning algorithms, fraudsters can be prevented from gaining illegal access to client accounts. Fraud on credit cards is more prevalent than ever.Since fraudsters work on a worldwide scale, something needs to be done to stop them. If certain acts were prohibited, the project's main goal—the recovery and restoration of client funds—would be accomplished, which would be beneficial to the clients. In addition, they disagreed with paying for unnecessary items or services.

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