Bibliometric insights into the intellectual dynamics of forensic accounting research

Figures & data

Table 1. Description of the query.

Category	Limitation	No. of refined documents
Query outcomes before search refine	All (‘Judicial Accounting’ Or ‘Accounting Curriculum’ Or ‘Financial Statement Fraud’ Or ‘Forensic Accountant’ Or ‘Forensic Accounting’ Or ‘Fraud Examination’ Or ‘Fraud Investigation’ Or ‘Fraud Triangle’ Or ‘Fraud Triangle Theory’)	7315.00
Access (Types including both open access and others)
Years	Including only years (2004–2023) documents (1,043)	(6272.00)
Subject area	372) Business Finance and Accounting (425) Business (141) Management (112) Law and Ethics (80) and Economics (69) Limit the search to the following subjects: We excluded engineering, computer, medical, biology and other subjects (372)	(372.00)
Document type	Limiting the documents to articles (563) and review (14); while excluded all other resources as chapters, Note, Erratum, Books and others (87)	(94.00)
Publication stage	Web of Science Index: Emerging Sources Citation Index (ESCI) (370) or Social Sciences Citation Index (SSCI) (155) or Science Citation Index Expanded (SCI-Expanded) (9)	(52.00)
Source type	We kept the journals (525)	(0)
Language	We excluded all languages other than English (486)	(39.00)
Final number refined search		486.00
Manual Refinement:	We reviewed the 486 documents titles and abstracts and excluded the irrelevant documents (218)	(218.00)
Final remaining documents after manual refine		268.00

Source: Author.

Table 2. Overview Summary.

An overview	Results
Data Principles
Duration	2004:2023
References (Journals, books, etc.)	39
Various documents	268
Growth Rate Percentage	12.93
Average age of documents	5.32
The average number of citations per document	23.26
Bibliography	2851
Contents of the Document
Keywords Plus (ID)	159
Author’s Keywords (DE)	163
Authors
Authors	126
Authors of single-authored docs	10
Authors Collaboration
Single-authored docs	10
Co-Authors per Document	2.66
International co-authorships %	36
Document Types
article	563
review	14

Source: Author.

Figure 1. Most significant countries.

Source: Author.

Table 3. Most significant institutions.

No	Affiliations	Articles
7	University of New South Wales Sydney	5
1	State University of New York Suny System	7
2	Deakin University	6
3	State University System of Florida	6
4	University System of Georgia	6
5	Macquarie University	5
6	State University of New York Suny Albany	5
8	University System of Ohio	5
9	California State University System	4
10	George Mason University	4

Source: Author.

Table 4. Most significant sources.

Rank	Journal name	No. of published articles	Total citation	Impact factor (2022)	JCR ranking-2022	Publisher
7	Accounting Organizations and Society	5	372	4.7	Q1	Elsevier
1	Journal of Business Ethics	32	866	6.1	Q2	Springer
2	Issues in Accounting Education	15	281	1.7	Q4	American Accounting Association
3	Accounting Education	13	211	3.2	Q2	Taylor & Francis
4	Journal of Emerging Technologies in Accounting	9	79	3	Q2	American Accounting Association
5	Accounting Research Journal	8	57	1.9	Q2	Emerald
6	Auditing a Journal of Practice Theory	6	369	3.1	Q2	American Accounting Association
8	Journal of Accounting and Public Policy	5	144	3.6	Q2	Elsevier
9	Accounting Forum	4	140	3.1	Q2	Taylor & Francis
10	Contemporary Accounting Research	4	187	3.6	Q2	Wiley

Source: Author.

Figure 2. Most significant authors.

Source: Author.

Figure 3. Law of Bradford’s.

Source: Author.

Figure 4. Most significant cited authors by countries.

Source: Author.

Table 5. Documents with the highest number of citations.

Authors	Title	Journal	Total citations
Perols, J. L., & Lougee, B. A	The relation between earnings management and financial statement fraud	Advances in Accounting	81
Hogan, C. E., Rezaee, Z., Riley Jr, R. A., & Velury, U. K.	Financial statement fraud: Insights from the academic literature	Auditing: A Journal of Practice & Theory	160
Perols, J.	Financial statement fraud detection: An analysis of statistical and machine learning algorithms	Auditing: A Journal of Practice & Theory	111
Morales, J., Gendron, Y., & Guénin- Paracini, H.	The construction of the risky individual and vigilant organization: A genealogy of the fraud triangle	Accounting, Organizations and Society	82
Perols, J. L., Bowen, R. M., Zimmermann, C., & Samba, B.	Finding needles in a haystack: Using data analytics to improve fraud prediction	The Accounting Review	71
Troy, C., Smith, K. G., & Domino, M. A.	CEO demographics and accounting fraud: Who is more likely to rationalize illegal acts?	Strategic Organization	62
Lisic, L. L., Silveri, S. D., Song, Y., & Wang, K.	Accounting fraud, auditing and the role of government sanctions in China	Journal of Business Research	55
Free, C.	Looking through the fraud triangle: A review and call for new directions	Meditari Accountancy Research	48
Hoogs, B., Kiehl, T., Lacomb, C., & Senturk, D.	A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud	Intelligent Systems in Accounting, Finance & Management: International Journal	42
Song, X. P., Hu, Z. H., Du, J. G., & Sheng, Z. H.	Application of machine learning methods to risk assessment of financial statement fraud: Evidence from China	Journal of Forecasting	37

Source: Author.

Table 6. Future directions of research.

Main Clusters	Sub-cluster	#	Questions	Sources
Cluster one: Financial statement fraud triangle	Holistic Understanding	1	What factors contribute to fraudulent operations in businesses?	Hogan et al. (2008), Free (2008), Nasir and Nabm (2018)
		2	How can a holistic understanding of financial statement fraud be emphasized?	Authors suggestion’s
		3	What emerging aspects of the fraud triangle are highlighted for fresh approaches?	Authors suggestion’s
		4	What are the implications and observations regarding financial statement fraud?	Authors suggestion’s
	Fraud Detection Methods	5	How do statistical and machine learning tools contribute to fraud detection?	Perols (2012), Perols et al. (2017), Perols and Lougee (2012), Song et al. (2011), Hoogs et al. (2017)
		6	What insights are provided regarding the link between earnings management and fraud?	Perols and Lougee (2012)
		7	How can machine learning methods be advocated for risk assessment in fraud prevention?	Song et al. (2011), Hoogs et al. (2017)
	Legal Repercussions	8	What legal repercussions are associated with financial statement fraud?	Lisic et al. (2011), Ghafoor (2019), Suh et al. (2020)
		9	How do Chinese government sanctions impact financial statement fraud?	Lisic et al. (2011), Gepp (2021)
	Corporate Factors	10	What is the connection between CEO demographics and the rationalization of illegal acts?	Troy et al. (2015)
		11	How does the relationship between vigilant organizations and risky individuals evolve within corporations?	Morales et al. (2017)
		12	What role do innovative techniques and sophisticated analytics play in fraud prevention?	Authors suggestion’s
		13	What are the implications of CEO demographics and government sanctions in fraud prevention?	Wang (2022), Gepp (2021)
	Education in Fraud Prevention	14	How can data analytics be incorporated into the accounting curriculum for fraud prevention?	Howieson (2018), Dzuranin et al. (2018)
		15	What insights are provided for educating professionals in the field of fraud prevention?	Authors suggestion’s
Cluster two: Corporate governance and fraud prevention	Dynamic Nature of Risk Assessment	16	How can machine learning and statistical tools contribute to dynamic risk assessment?	Hogan et al. (2008), Perols (2012)
		17	What insights are gained from dissecting the ‘fraud triangle’ in understanding risk dynamics?	Morales et al. (2017), Free (2008)
	Earnings Management and Fraud	18	How is the intricate link between financial statement fraud and earnings management analysed?	Perols (2012), Perols et al. (2017)
		19	In what ways can data analytics be a crucial tool for predicting fraud?	Authors suggestion’s
		20	How do government sanctions impact accounting fraud, particularly in China?	Lisic et al. (2011), Gepp (2021)
		21	How does the incorporation of machine learning in risk assessment reflect evolving accounting practices?	Song et al. (2011), Albrecht (2015a,2015b)
	Proactive Corporate Measures	22	How is the link between CEO demographics and justifying unlawful activities examined?	Troy et al. (2015)
		23	What insights are gained regarding the legal aspects of detecting financial statement deceit?	Smaili and Arroyo (2019), Ghafoor (2019)
		24	How effective are government sanctions in deterring fraud in Chinese corporations?	Gepp (2021)
		25	How can proactive corporate measures be implemented using a genetic algorithm for fraud detection?	Hoogs et al. (2017)
		26	What is the relationship between CEO demographics and the propensity for fraud?	Wang (2022), Troy et al. (2015)
	Corporate Governance in Fraud Prevention	27	How is financial statement fraud examined within the broader corporate context?	Nasir and Nabm (2018), Hoogs et al. (2017)
		28	What insights are offered regarding the complexities of corporate governance in fraud prevention?	Authors suggestion’s
Cluster three: Fraud detection techniques and methodologies	Detection Strategies	29	What specific strategies are employed in detecting financial statement fraud?	Perols and Lougee (2012), Lisic et al. (2011), Hoogs et al. (2017), Nasir and Nabm (2018), Albrecht (2015a, 2015b)
		30	How is the link between earnings management and fraud established?	Authors suggestion’s
		31	What is the impact of government sanctions on auditing and accounting misconduct?	Lisic et al. (2011)
		32	How effective is a genetic algorithm in identifying temporal patterns indicative of fraud?	Hoogs et al. (2017)
		33	What literature-based insights are provided into financial statement fraud?	Nasir and Nabm (2018)
		34	What factors contribute to the relationship between ographics CEO dem and fraud propensity?	Troy et al. (2015)
		35	How is the legal lineage of the fraud triangle traced in examining fraud risk factors?	Smaili and Arroyo (2019)
		36	How are statistical and machine learning algorithms employed in Fraud detection?	Ghafoor (2019)
		37	What legal connections exist between earnings management and fraud?	Suh et al. (2020), Wang (2022)
		38	How is machine learning applied in assessing fraud risk in China?	Song et al. (2011)
		39	What power does data analytics hold in enhancing fraud prediction?	Perols et al. (2017)
		40	How effective are government sanctions in deterring fraud in corporations?	Authors suggestion’s
		41	How can data analytics methods be improved for predicting fraud?	Mui (2015)
		42	How is a genetic	Howieson (2018),
			algorithm employed to identify suspicious	Alden (2012)
			patterns?
		43	In what ways can machine	Alden (2012)
			learning be used for fraud
			risk assessment?
		44	How can police be	Brooks and Button (2011)
			involved in fraud
			investigations?
		45	What role does	Authors suggestion’s
			incorporating data
			analytics into the
			accounting
			curriculum play in fraud
			detection?
		46	What specific detection strategies are employed in addressing financial statement fraud?	Perols and Lougee (2012), Lisic et al. (2011), Hoogs et al. (2017), Nasir and Nabm (2018), Albrecht (2015a, 2015b)
		47	How is the link between earnings management and fraud established?	Perols and Lougee (2012)
		48	What factors contribute to the relationship between CEO demographics and fraud propensity?	Troy et al. (2015)
		49	How is the legal lineage of the fraud triangle traced in examining fraud risk factors?	Smaili and Arroyo (2019)
		50	How are statistical and machine learning algorithms employed in fraud detection?	Ghafoor (2019)
		51	What legal connections exist between earnings management and fraud?	Authors suggestion’s
		52	How is machine learning applied in assessing fraud risk?	Song et al. (2011)
		53	What power does data analytics hold in enhancing fraud prediction?	Perols et al. (2017)
		54	How effective are government sanctions in deterring fraud in Chinese corporations?	Gepp (2021)
		55	How can data analytics methods be improved for predicting fraud?	Mui (2015)
		56	How is a genetic algorithm employed to identify suspicious patterns?	Howieson (2018), Alden (2012)
		57	In what ways can machine learning be used for fraud risk assessment?	Alden (2012)
		58	What role does incorporating data analytics into the accounting curriculum play in fraud detection?	Authors suggestion’s

Source: Author.

Figure 5. Keywords/cartography analysis.

Source: Author.

Figure 6. Keywords/growth analysis.

Source: Author.

Figure 7. Publications per cluster.

Hogan, C. E., Rezaee, Z., Riley, R. A., Jr. & Velury, U. K. (2008). Financial statement fraud: Insights from the academic literature. AUDITING: A Journal of Practice & Theory, 27(2), 231–252. https://doi.org/10.2308/aud.2008.27.2.205

 Web of Science ®Google Scholar

Free, C. (2015). Looking through the fraud triangle: A review and call for new directions. Meditari Accountancy Research, 23(2), 175–196. https://doi.org/10.1108/MEDAR-02-2015-0009

 Google Scholar

Nasir, N., & Nabm. (2018). Financial statement fraud: Insights from the academic literature. International Journal of Accounting Information Management, 23(2), 3.83–2.38. https://doi.org/10.1108/IJAIM-03-2017-0039

 Google Scholar

Perols, J. (2012). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 31(1), 1–32. https://doi.org/10.2308/ajpt-10209

 Google Scholar

Perols, J. L., Bowen, R. M., Zimmermann, C., & Samba, B. (2017). Finding needles in a haystack: Using data analytics to improve fraud prediction. The Accounting Review, 92(2), 221–245. https://doi.org/10.2308/accr-51727

 Web of Science ®Google Scholar

Perols, J. L., & Lougee, B. A. (2012). The relation between earnings management and financial statement fraud. Advances in Accounting, 27(1), 39–53. https://doi.org/10.1016/j.adiac.2012.01.004

 Google Scholar

Song, X. P., Hu, Z. H., Du, J. G., & Sheng, Z. H. (2011). Application of machine learning methods to risk assessment of financial statement fraud: Evidence from China. Journal of Forecasting, 30(3), 293–317. https://doi.org/10.1002/for.1203

 Google Scholar

Hoogs, B., Kiehl, T., Lacomb, C., & Senturk, D. (2017). A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud. Intelligent systems in accounting. Finance & Management: International Journal, 24(1–2), 23–47. https://doi.org/10.1002/isaf.1426

 Google Scholar

Lisic, L. L., Silveri, S. D., Song, Y., & Wang, K. (2011). Accounting fraud, auditing, and the role of government sanctions in China. Journal of Business Research, 64(3), 322–331. https://doi.org/10.1016/j.jbusres.2010.01.016

 Google Scholar

Ghafoor, A. (2019). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Journal of Business Ethics, 19(1), 3.80–1.61. https://doi.org/10.1007/s10551-018-3877-3

 Google Scholar

Suh, I., Sweeney, J. T., Linke, K., & Wall, J. M. (2020). The relation between earnings management and financial statement fraud. Journal of Business Ethics, 162(3), 645–673. https://doi.org/10.1007/s10551-018-3982-3

 Google Scholar

Gepp, A. (2021). Accounting fraud, auditing, and the role of government sanctions in China. Accounting & Finance, 13(1), 4.33–2.25. https://doi.org/10.1111/acfi.12742

 Google Scholar

Troy, C., Smith, K. G., & Domino, M. A. (2015). CEO demographics and accounting fraud: Who is more likely to rationalize illegal acts? Strategic Organization, 13(1), 1–29. https://doi.org/10.1177/1476127014549441

 Google Scholar

Morales, J., Gendron, Y., & Guénin-Paracini, H. (2017). The construction of the risky individual and vigilant organization: A genealogy of the fraud triangle. Accounting, Organizations and Society, 60, 62–81. https://doi.org/10.1016/j.aos.2017.07.003

 Google Scholar

Wang, Y. (2022). CEO demographics and accounting fraud: Who is more likely to rationalize illegal acts? Journal of Accounting and Public Policy, 16(1), 8.00–2.86. https://doi.org/10.1016/j.jaccpubpol.2021.106903

 Google Scholar

Howieson, B. (2018). A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud. Pacific Accounting Review, 13(1), 2.17–1.34. https://doi.org/10.1108/PAR-01-2017-0005

 Google Scholar

Dzuranin, A. C., Jones, J. R., & Olvera, R. M. (2018). Infusing data analytics into the accounting curriculum: A framework and insights from faculty. Journal of Accounting Education, 43, 24–39. https://doi.org/10.1016/j.jaccedu.2018.03.004

 Google Scholar

Albrecht, C., Holland, D., Malagueño, R., Dolan, S., & Tzafrir, S. (2015a). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Journal of Business Ethics, 131(4), 803–813. https://doi.org/10.1007/s10551-013-2019-1

 Google Scholar

Smaili, N., & Arroyo, P. (2019). The construction of the risky individual and vigilant organization: A genealogy of the fraud triangle. Journal of Business Ethics, 157(1), 95–117. https://doi.org/10.1007/s10551-017-3663-7

 Google Scholar

Mui, G. (2015). Finding needles in a haystack: Using data analytics to improve fraud prediction. Accounting Research Journal, 18(1), 2.00–0.50. https://doi.org/10.1108/ARJ-10-2014-0092

 Google Scholar

Alden, M. E. (2012). Application of machine learning methods to risk assessment of financial statement fraud: Evidence from China. Journal of Emerging Technologies in Accounting, 12(1), 1.00–1.00. https://doi.org/10.2308/jeta-50390

 Google Scholar

Brooks, G., & Button, M. (2011). The police and fraud investigation and the case for a nationalised solution in the United Kingdom. The Police Journal, 84(4), 305–319. https://doi.org/10.1350/pojo.2011.84.4.559

 Google Scholar

Data availability statement

No data were generated or used in this work. The study did not involve the collection or analysis of original data. Therefore, there is no data available for sharing.