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Using Data Mining Techniques in Fiscal Fraud Detection
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Using Data Mining Techniques in Fiscal Fraud Detection
F. Bonchi7, 6 , F. Giannotti6 , G. Mainetto6 and D. Pedreschi7 
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CNUCE—CNR, Via S. Maria 36, 56126 Pisa |
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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.
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