Risk based Government Audit Planning using Naïve Bayes Classifiers

Abstract : 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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https://hal.archives-ouvertes.fr/hal-00746198
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Submitted on : Sunday, November 25, 2012 - 12:46:02 PM
Last modification on : Thursday, October 11, 2018 - 8:48:02 AM
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Remis Balaniuk, Pierre Bessiere, Emmanuel Mazer, Paulo Cobbe. Risk based Government Audit Planning using Naïve Bayes Classifiers. Advances in Knowledge-Based and Intelligent Information and Engineering Systems, 2012, Spain. ⟨10.3233/978-1-61499-105-2-1313⟩. ⟨hal-00746198⟩

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