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SNIPER: A Data Mining Methodology for Fiscal Fraud Detection

Academic Article
Publication Date:
2009
abstract:
An effective audit strategy is a key success factor for 'a posteriori' fraud detection applications in fiscal and insurance domains. 'Sniper' is an auditing methodology with a rule-based system, which is capable of dealing with conflicting issues such as maximizing audit benefits, minimizing false-positive audit predictions and deterring probable future fraud.
Iris type:
01.01 Articolo in rivista
Keywords:
Data Mining; Fiscal Fraud Detection
List of contributors:
Spinsanti, Laura; Manco, Giuseppe; Giannotti, Fosca; Basta, Stefano
Authors of the University:
BASTA STEFANO
MANCO GIUSEPPE
Handle:
https://iris.cnr.it/handle/20.500.14243/36627
Published in:
ERCIM NEWS
Journal
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