Risk based Government Audit Planning using Näıve Bayes Classifiers

نویسندگان

  • Remis Balaniuk
  • Pierre Bessiere
  • Emmanuel Mazer
  • Paulo Cobbe
چکیده

In this paper we consider the application of a naïve Bayes model for the evaluation of fraud risk connected with government agencies. This model applies probabilistic classifiers to support a generic risk assessment model, allowing for more efficient and effective use of resources for fraud detection in government transactions, and assisting audit agencies in transitioning from reactive to proactive fraud detection model.

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تاریخ انتشار 2015