Financial Statement Fraud: Insights from the Academic Literature

被引：166

作者：

Hogan, Chris E. ^{[1
]}

Rezaee, Zabihollah ^{[2
]}

Riley, Richard A., Jr. ^{[3
]}

Velury, Uma K. ^{[4
]}

机构：

[1] Michigan State Univ, E Lansing, MI 48824 USA

[2] Univ Memphis, Memphis, TN 38152 USA

[3] W Virginia Univ, Morgantown, WV 26506 USA

[4] Univ Delaware, Newark, DE 19716 USA

来源：

AUDITING-A JOURNAL OF PRACTICE & THEORY | 2008年 / 27卷 / 02期

关键词：

financial statement fraud; fraud detection; fraud triangle; audit procedures; audit planning; high-risk audit areas;

D O I：

10.2308/aud.2008.27.2.231

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

We summarize relevant academic research findings to contribute to the Public Company Accounting Oversight Board (PCAOB) project on financial statement fraud and to offer insights and conclusions relevant to academics, standard setters, and practitioners. We discuss the characteristics of firms committing financial statement fraud, as identified in the literature, and research related to the fraud triangle. We then discuss research related to the procedures and abilities of auditors to detect fraud, and how fraud risk assessments impact audit planning and testing. In addition, we discuss several "high risk" areas and other issues as identified by the PCAOB. Finally, we summarize prior findings and offer conclusions and suggestions for areas where future research is needed.

引用

页码：231 / 252

页数：22

共 50 条

[31] Increasing the Detection of Minority Class Instances in Financial Statement Fraud [J].

Moepya, Stephen Obakeng ;

Nelwamondo, Fulufhelo V. ;

Twala, Bhekisipho .

INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2017), PT II, 2017, 10192 :33-43

[32] The role of social control in deterring corporate financial statement fraud [J].

Dewi Nur Maulidiyah ;

Puji Harto .

Discover Global Society, 3 (1)

[33] Uncovering Financial Statement Fraud: A Machine Learning Approach With Key Financial Indicators and Real-World Applications [J].

Li, Bixuan ;

Yen, Jerome ;

Wang, Sheng .

IEEE ACCESS, 2024, 12 :194859-194870

[34] BEYOND THE NUMBERS: MINING THE ANNUAL REPORTS FOR HIDDEN CUES INDICATIVE OF FINANCIAL STATEMENT FRAUD [J].

Goel, Sunita ;

Gangolly, Jagdish .

INTELLIGENT SYSTEMS IN ACCOUNTING FINANCE & MANAGEMENT, 2012, 19 (02) :75-89

[35] A Deep Neural Network Based Financial Statement Fraud Detection Model: Evidence from China [J].

Wang, Yurou ;

Li, Ruixue ;

Niu, Yanfang .

AICCC 2021: 2021 4TH ARTIFICIAL INTELLIGENCE AND CLOUD COMPUTING CONFERENCE, 2021, :145-149

[36] Evaluating Machine Learning Algorithms for Financial Fraud Detection: Insights from Indonesia [J].

Lee, Cheng-Wen ;

Fu, Mao-Wen ;

Wang, Chin-Chuan ;

Azis, Muh. Irfandy .

MATHEMATICS, 2025, 13 (04)

[37] Application of Machine Learning Methods to Risk Assessment of Financial Statement Fraud: Evidence from China [J].

Song, Xin-Ping ;

Hu, Zhi-Hua ;

Du, Jian-Guo ;

Sheng, Zhao-Han .

JOURNAL OF FORECASTING, 2014, 33 (08) :611-626

[38] A framework for detecting financial statement fraud through multiple data sources [J].

Dillon, Darshan ;

Hadzic, Maja .

2009 3RD IEEE INTERNATIONAL CONFERENCE ON DIGITAL ECOSYSTEMS AND TECHNOLOGIES, 2009, :665-669

[39] Research on the Optimization of Listed Companies' Legal Responsibility in Financial Statement Fraud [J].

Li, Hangmin .

PROCEEDINGS OF THE 2017 3RD INTERNATIONAL CONFERENCE ON SOCIAL SCIENCE AND HIGHER EDUCATION, 2017, 99 :173-178

[40] Empirical research on the relationship between internal corporate governance and financial statement fraud [J].

Wang, X ;

Liang, J ;

Li, JZ .

ISMOT'04: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON MANAGEMENT OF INNOVATION AND TECHNOLOGY: MANAGING TOTAL INNOVATION IN THE 21ST CENTURY, 2004, :117-120

← 1 2 3 4 5 →