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- W4327499525 abstract "In the current economy, credit cards are an important factor. It becomes an essential component of domestic, commercial, and international activities. Although using credit cards responsibly and reliably offers enormous benefits, fraudulent activity has the potential to cause significant credit and financial harm. Many strategies have been put out to combat the rise in credit card fraud. The detection of credit card theft is currently the most well-known issue. Companies that issue credit cards are searching for the greatest tools and conditions to spot and lessen fraudulent credit card transactions. Recently, AI has gained some attention in the areas of photo analysis, natural language processing, and speech recognition. In order to mitigate these setbacks and assist fraud specialists, effective fraud detection calculations leveraging AI technologies are essential. In this study, credit card theft is detected using an AI-based calculated relapse technique. The findings indicate that computed relapse-based techniques outperform with the highest level of accuracy, and they may very well be used in practice by fraud examiners." @default.
- W4327499525 created "2023-03-17" @default.
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- W4327499525 date "2022-12-26" @default.
- W4327499525 modified "2023-09-27" @default.
- W4327499525 title "Analysis of Fraud Credit Transaction and its Detection" @default.
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- W4327499525 doi "https://doi.org/10.1109/icerect56837.2022.10060262" @default.
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