The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Data Mining techniques for the detection of fraudulent financial statements
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Citations
Cites
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY
Altman, E. I. (1968) · The Journal of Finance · doi:10.1111/j.1540-6261.1968.tb00843.x · Cited by 14,888
(1997) · CFA Digest · doi:10.2469/dig.v27.n2.79 · Cited by 3,518
Data Mining: Concepts, Models, Methods, and Algorithms
(2003) · Technometrics · doi:10.1198/tech.2003.s785 · Cited by 1,369
The Financial and Market Effects of the SEC's Accounting and Auditing Enforcement Releases
Feroz, E. H., Park, K. J., & Pastena, V. S. (1991) · Journal of Accounting Research · doi:10.2307/2491006 · Cited by 688
The effects of audit committee activity and independence on corporate fraud
Abbott, L. J., Park, Y., & Parker, S. (2000) · Managerial Finance · doi:10.1108/03074350010766990 · Cited by 635
Data Mining: Concepts, Models, Methods, and Algorithms
SRIVASTAVA, A. N. (2005) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.2123107 · Cited by 512
Characteristics of firms correcting previously reported quarterly earnings
Kinney, W. R., & McDaniel, L. S. (1989) · Journal of Accounting and Economics · doi:10.1016/0165-4101(89)90014-1 · Cited by 486
Fraudulently Misstated Financial Statements and Insider Trading: An Empirical Analysis.
Summers, S. L., & Sweeney, J. T. (1998) · The Accounting Review · doi:10.2308/tar-274320 · Cited by 480
A Decision Aid for Assessing the Likelihood of Fraudulent Financial Reporting
Bell, T. B., & Carcello, J. V. (2000) · AUDITING: A Journal of Practice & Theory · doi:10.2308/aud.2000.19.1.169 · Cited by 437
Cited by
Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.08.006 · Cited by 1,361
Social big data: recent achievements and new challenges
Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016) · Information Fusion · doi:10.1016/j.inffus.2015.08.005 · Cited by 846
“[103] Identification of fraudulent financial statements Classification (decision trees, neural networks”
BackgroundData mining techniques and applications – A decade review from 2000 to 2011
Liao, S.-H., Chu, P.-H., & Hsiao, P.-Y. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.02.063 · Cited by 665
Intelligent financial fraud detection: A comprehensive review
West, J., & Bhattacharya, M. (2016) · Computers & Security · doi:10.1016/j.cose.2015.09.005 · Cited by 592
Impact of business analytics and enterprise systems on managerial accounting
Appelbaum, D., Kogan, A., Vasarhelyi, M., & Yan, Z. (2017) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2017.03.003 · Cited by 554
Metadata
- Authors
- KIRKOS, E., SPATHIS, C., & MANOLOPOULOS, Y.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2007-05
- Volume
- 32
- Issue
- 4
- Pages
- 995-1003
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2085573882
- Cited by
- 722
- References
- 25
- FWCI
- 14.03
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- KIRKOS, E., SPATHIS, C., & MANOLOPOULOS, Y. (2007). Data Mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2006.02.016
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
