Advertisement

Collusion and Corruption Risk Analysis Using Naïve Bayes Classifiers

  • Remis Balaniuk
  • Pierre Bessiere
  • Emmanuel Mazer
  • Paulo Cobbe
Part of the Communications in Computer and Information Science book series (CCIS, volume 246)

Abstract

Fighting corruption connected with public procurement and governmental agencies requires a strong and effective audit function. The scale and the complexity of the roles to be considered can prevent the use of most audit methods and technologies so successful on the corporate world. The aim of this chapter is to propose a data mining method, based on naïve Bayes classifiers, to support a generic risk assessment process for audit planning. The method can sort auditable units by total risk score, fostering dedicated audit coverage to high-risk areas. Audit organizations can transition from a reactive response to a proactive approach to identify and correct issues that may be indicative of fraud, waste or abuse. Extensive databases containing records from government operations can be combined to auditors knowledge, fraud profiles, impact factors or any other relevant metric in order to rank an audit universe.

Keywords

probabilistic classifiers data mining public sector corruption risk analysis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Coderre, D.: Auditing - Computer-Assisted Techniques for Fraud Detection. The CPA Journal (August 1999)Google Scholar
  2. 2.
    United States Government Accountability Office (GAO): Government Auditing Standards (The Yellow Book) (August 2011)Google Scholar
  3. 3.
    Vito, K.W.: SPHR, CCP, Auditing Employee Hiring and Staffing. The IIA Research Foundation (June 2011)Google Scholar
  4. 4.
    The Institute of Internal Auditors: International Professional Practices Framework (IPPF). The IIA Research Foundation (2009)Google Scholar
  5. 5.
    Ngai, E.W.T., Yong Hu, Y.H., Wong, Y.C., Sun, X.: The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 559–569 (2011)Google Scholar
  6. 6.
    Panigrahi, S., Kundu, A., Sural, S., Majumdar, A.K.: Credit card fraud detection: A fusion approach using Dempster Shafer theory and Bayesian learning. Information Fusion 10(4), 354–363 (2009)CrossRefGoogle Scholar
  7. 7.
    Phua, C., Lee, V., Smith, K., Gayler, R.: A comprehensive survey of data mining-based fraud detection research. Artificial Intelligence Review, 1–14 (2005)Google Scholar
  8. 8.
    Hormazi, A.M., Giles, S.: Data Mining: A Competitive Weapon for Banking and Retail Industries. Information Systems Management 21(2), 62–71 (2004)CrossRefGoogle Scholar
  9. 9.
    Nilsen, T., Aven, T.: Models and model uncertainty in the context of risk analysis. Reliability Engineering and System Safety 79, 309–331 (2003)CrossRefGoogle Scholar
  10. 10.
    Nilsen, T.: Foundations of Risk Analysis: A Knowledge and Decision-Oriented Perspective. John Wiley and Sons Ltd., West Sussex (2003)Google Scholar
  11. 11.
    Viaene, S., Derrig, R.A., Dedene, G.: A Case Study of Applying Boosting Naive Bayes to Claim Fraud Diagnosis. IEEE Transactions on Knowledge and Data Engineering 16(5), 612–620 (2004)CrossRefGoogle Scholar
  12. 12.
    Viaene, S., Derrig, R.A., Baesens, B., Dedene, G.: A Comparison of State-of-the-Art Classification Techniques for Expert Automobile Insurance Claim Fraud Detection. J. Risk and Insurance 69(3), 373–421 (2002)CrossRefGoogle Scholar
  13. 13.
    Organisation for Economic Co-operation and Development: Roundtable on Collusion and Corruption in Public Procurement (October 2010)Google Scholar
  14. 14.
    Mekhnacha, K., Ahuactzin, J.M., Bessiere, P., Mazer, E., Smail, L.: Exact and approximate inference in ProBT. Revue d’Intelligence Artificielle 21(3), 295–332 (2007)CrossRefGoogle Scholar
  15. 15.
    Zhang, H.: The Optimality of Naive Bayes. American Association for Artificial Intelligence (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Remis Balaniuk
    • 1
    • 2
  • Pierre Bessiere
    • 3
  • Emmanuel Mazer
    • 4
  • Paulo Cobbe
    • 5
  1. 1.MGCTICatholic University of BrasiliaBrasília DFBrazil
  2. 2.Setor de Administracão Federal SulTribunal de Contas da UniãoBrasília - DFBrazil
  3. 3.LPPA - Collège de FranceParis cedex05France
  4. 4.CNRS, E-Motion, LIG - INRIAMontbonnotFrance
  5. 5.Information Technology DepartmentUniCEUB CollegeBrasília - DFBrazil

Personalised recommendations