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Using Data Mining Techniques in Fiscal Fraud Detection

F. Bonchi7, 6 Contact Information, F. GiannottiContact Information, G. MainettoContact Information and D. PedreschiContact Information

(6)  CNUCE—CNR, Via S. Maria 36, 56126 Pisa
(7)  Dipartimento di Informatica, Università di Pisa, C.so Italia 40, 56125 Pisa
Abstract
Planning adequate audit strategies is a key success factor in “a posteriori” fraud detection, e.g., in the fiscal and insurance domains, where audits are intended to detect tax evasion and fraudulent claims. A case study is presented in this paper, which illustrates how techniques based on classification can be used to support the task of planning audit strategies. The proposed approach is sensible to some conflicting issues of audit planning, e.g., the trade-off between maximizing audit benefits vs. minimizing audit costs.

Contact Information F. Bonchi
Email: bonchi@di.unipi.it

Contact Information F. Giannotti
Email: F.Giannotti@cnuce.cnr.it

Contact Information G. Mainetto
Email: G.Mainetto@cnuce.cnr.it

Contact Information D. Pedreschi
Email: pedre@di.unipi.it
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Referenced by
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  1. Benoît, Gerald (2002) Data mining. Annual Review of Information Science and Technology 36(1)
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