Credit Card Fraud Prediction System
DOI:
https://doi.org/10.53555/nncse.v2i3.481Keywords:
raud Prediction, Communal Detection (CD), pike Detection (CD)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.
References
“Resilient Identity Crime Detection” by Clifton Phua, Kate Smith-Miles,Vincent Lee, and Ross Gayler.
“Adaptive Fraud Detection” by Tom Fawcett, Foster Provost.
“A Taxonomy of Fraud and Fraud Detection Techniques” by NaeimehLaleh, Mohammad AbdollahiAzgomi.
“Credit Card Fraud Prediction Uses Bayesian and Neural Networks” by Sam Maes, Karl Tuyls, Bram Vanschoenwinkel
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