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2009 IEEE International Conference on Data Mining Workshops
High Quality True-Positive Prediction for Fiscal Fraud Detection
Miami, Florida, USA
December 06-December 06
ISBN: 978-0-7695-3902-7
ASCII Text
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Stefano Basta, Fabio Fassetti, Massimo Guarascio, Giuseppe Manco, Fosca Giannotti, Dino Pedreschi, Laura Spinsanti, Gianfilippo Papi, Stefano Pisani, "High Quality True-Positive Prediction for Fiscal Fraud Detection," Data Mining Workshops, International Conference on, pp. 7-12, 2009 IEEE International Conference on Data Mining Workshops, 2009.
 
 
BibTex
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@article{ 10.1109/ICDMW.2009.59,
author = {Stefano Basta and Fabio Fassetti and Massimo Guarascio and Giuseppe Manco and Fosca Giannotti and Dino Pedreschi and Laura Spinsanti and Gianfilippo Papi and Stefano Pisani},
title = {High Quality True-Positive Prediction for Fiscal Fraud Detection},
journal ={Data Mining Workshops, International Conference on},
volume = {0},
year = {2009},
isbn = {978-0-7695-3902-7},
pages = {7-12},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDMW.2009.59},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Data Mining Workshops, International Conference on
TI - High Quality True-Positive Prediction for Fiscal Fraud Detection
SN - 978-0-7695-3902-7
SP7
EP12
A1 - Stefano Basta,
A1 - Fabio Fassetti,
A1 - Massimo Guarascio,
A1 - Giuseppe Manco,
A1 - Fosca Giannotti,
A1 - Dino Pedreschi,
A1 - Laura Spinsanti,
A1 - Gianfilippo Papi,
A1 - Stefano Pisani,
PY - 2009
VL - 0
JA - Data Mining Workshops, International Conference on
ER -
 
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.
Citation:
Stefano Basta, Fabio Fassetti, Massimo Guarascio, Giuseppe Manco, Fosca Giannotti, Dino Pedreschi, Laura Spinsanti, Gianfilippo Papi, Stefano Pisani, "High Quality True-Positive Prediction for Fiscal Fraud Detection," icdmw, pp.7-12, 2009 IEEE International Conference on Data Mining Workshops, 2009
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