Risk based Government Audit Planning using Naive Bayes Classifiers`

被引：3

作者：

Balaniuk, Remis ^{[1
,2
]}

Bessiere, Pierre ^{[3
,4
]}

Mazer, Emmanuel ^{[4
]}

Cobbe, Paulo ^{[1
,5
]}

机构：

[1] Univ Catolica Brasilia, MGCTI, SGAN 916 Modulo B Asa Norte, BR-70790160 Brasilia, DF, Brazil

[2] Tribunal Contas Uniao, Setor Adm Fed Sul, BR-70042900 Brasilia, DF, Brazil

[3] LPPA Coll France, Dallas, TX 75231 USA

[4] CNRS, LIG INRIA, F-38334 Montbonnot St Martin, France

[5] UniCEUB Coll, Dept Informat Technol, BR-70790075 Brasilia, DF, Brazil

来源：

ADVANCES IN KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS | 2012年 / 243卷

关键词：

Data mining; naive Bayes; fraud detection; FRAUD DETECTION;

D O I：

10.3233/978-1-61499-105-2-1313

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we consider the application of a naive 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.

引用

页码：1313 / 1323

页数：11

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