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- W2783200340 abstract "Identity crime is common, and pricey, and credit card fraud is a specific case of identity crime. The existing systems of known fraud matching and business rules have restrictions. To remove these negative aspects in real world, this paper proposes a data mining approach: Communal Detection (CD) and Spike Detection (SD). CD finds real social relationships to reduce the suspicion score, and is impervious to fake social relationships. This approach on a fixed set of attributes is whitelist-oriented. SD increases the suspicion score by finding discrepancies in duplicates. These data mining approaches can detect more types of attacks and removes the unnecessary attributes" @default.
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- W2783200340 date "2015-03-31" @default.
- W2783200340 modified "2023-09-25" @default.
- W2783200340 title "Credit Card Fraud Prediction System" @default.
- W2783200340 hasPublicationYear "2015" @default.
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