High Quality True-Positive Prediction for Fiscal Fraud Detection (Contributo in atti di convegno)

Type
Label
  • High Quality True-Positive Prediction for Fiscal Fraud Detection (Contributo in atti di convegno) (literal)
Anno
  • 2009-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/ICDMW.2009.59 (literal)
Alternative label
  • Basta Stefano; Fassetti Fabio; Giannotti Fosca; Guarascio Massimo; Manco Giuseppe; Papi Gianfilippo Maria; Pedreschi Dino; Pisani Stefano; Spinsanti Laura (2009)
    High Quality True-Positive Prediction for Fiscal Fraud Detection
    in International Workshop on Domain Driven Data Mining, Miami
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Basta Stefano; Fassetti Fabio; Giannotti Fosca; Guarascio Massimo; Manco Giuseppe; Papi Gianfilippo Maria; Pedreschi Dino; Pisani Stefano; Spinsanti Laura (literal)
Note
  • Google Scholar (literal)
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • ICAR-CNR; ICAR-CNR; ISTI-CNR; ICAR-CNR; ICAR-CNR; SOGEI SPA; UNIVERSITA' DI PISA; Agenzia delle Entrate; ISTI-CNR (literal)
Titolo
  • High Quality True-Positive Prediction for Fiscal Fraud Detection (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-4244-5384-9 (literal)
Abstract
  • In this paper we describe an experience resulting from the collaboration among Data Mining researchers, domain experts of the Italian Revenue Agency, and IT professionals, aimed at detecting fraudulent VAT credit claims. The outcome is an auditing methodology based on a rule-based system, which is capable of trading among conflicting issues, such as maximizing audit benefits, minimizing false positive audit predictions, or deterring probable upcoming frauds. We describe the methodology in detail, and illustrate its practical effectiveness compared to classical predictive systems from the literature. (literal)
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