Skip to main content

Using Data Mining Techniques in Fiscal Fraud Detection

  • Conference paper
  • First Online:
DataWarehousing and Knowledge Discovery (DaWaK 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1676))

Included in the following conference series:

  • 940 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Fawcett, T, Provost, F., “Adaptive Fraud Detection”, Data Mining and Knowledge Discovery, Vol. 1, No. 1, pp. 291–316, (1997).

    Article  Google Scholar 

  2. Uthurusamy, R., “From Data Mining to Knowledge Discovery: Current Challenges and Future Directions”, in Knowledge Discovery in Databases, Piatesky-Shapiro and Frawley (eds.), AAAI Press, Menlo Park, CA, (1991).

    Google Scholar 

  3. Fawcett, T, Provost, F., “Robust Classification Systems for Imprecise Environment”, Proc of the 15th Int. Conf. AAAI-98, (1998).

    Google Scholar 

  4. Stolfo, S., Fan, D., Lee, W., Prodromidis, A., Chan, P., “Credit Card Fraud detection using Metalearning: Issues and Initial Results”, Working Notes AAAI-97, (1997).

    Google Scholar 

  5. Tanzi, V., Shome, P., “A Primer on Tax Evasion”, in IMF Staff Papers, No 4, (1993).

    Google Scholar 

  6. Berry, M., Linoff, G., Data Mining Techniques for Marketing, Sales and Customer Support, Wiley Computer Publishing, New York, USA (1997).

    Google Scholar 

  7. Breiman, L., Friedman, J. H., Olshen, R. A., Stone, P. J., Classification and regression trees, Belmont, CA, Wadsworth (1984).

    Google Scholar 

  8. Indurkhya, N., Weiss, S. M., Predictive Datamining: a pratical guide, Morgan Kaufman, San francisco, CA, (1998).

    Google Scholar 

  9. Freund, Y., “Boosting a Weak Learning Algorithm by Majority”, Information and Computation, 121(2), pp. 256–285, (1995).

    Article  MATH  MathSciNet  Google Scholar 

  10. Bonchi, F., Giannotti, F., Mainetto, G., Pedreschi, D., “A classification-based methodology for planning auditing strategies in fraud detection”, accepted at KDD’99.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bonchi, F., Giannotti, F., Mainetto, G., Pedreschi, D. (1999). Using Data Mining Techniques in Fiscal Fraud Detection. In: Mohania, M., Tjoa, A.M. (eds) DataWarehousing and Knowledge Discovery. DaWaK 1999. Lecture Notes in Computer Science, vol 1676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48298-9_39

Download citation

  • DOI: https://doi.org/10.1007/3-540-48298-9_39

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66458-1

  • Online ISBN: 978-3-540-48298-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics