Data mining techniques for the detection of fraudulent financial statements

被引：306

作者：

Kirkos, Efstathios

Spathis, Charalambos ^{[1
]}

Manolopoulos, Yannis

机构：

[1] Aristotle Univ Thessaloniki, Dept Econ, Div Business Adm, Thessaloniki 54124, Greece

[2] Technol Educ Inst Thessaloniki, Dept Accounting, Thessaloniki 57400, Greece

[3] Aristotle Univ Thessaloniki, Dept Informat, Thessaloniki 54124, Greece

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2007年 / 32卷 / 04期

关键词：

fraudulent financial statements; management fraud; data mining; auditing; Greece; MODEL;

D O I：

10.1016/j.eswa.2006.02.016

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper explores the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. In accomplishing the task of management fraud detection, auditors could be facilitated in their work by using Data Mining techniques. This study investigates the usefulness of Decision Trees, Neural Networks and Bayesian Belief Networks in the identification of fraudulent financial statements. The input vector is composed of ratios derived from financial statements. The three models are compared in terms of their performances. (C) 2006 Elsevier Ltd. All rights reserved.

引用

页码：995 / 1003

页数：9

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