The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Detection of financial statement fraud and feature selection using data mining techniques
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches
Aamodt, A., & Plaza, E. (1994) · AI Communications · doi:10.3233/aic-1994-7104 · Cited by 5,624
Financial Ratios As Predictors of Failure
Beaver, W. H. (1966) · Journal of Accounting Research · doi:10.2307/2490171 · Cited by 4,882
Cited by
A survey of multiple classifier systems as hybrid systems
Woźniak, M., Graña, M., & Corchado, E. (2014) · Information Fusion · doi:10.1016/j.inffus.2013.04.006 · Cited by 912
Data 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
Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach
BAO, Y., KE, B., LI, B., YU, Y. J., & ZHANG, J. (2020) · Journal of Accounting Research · doi:10.1111/1475-679x.12292 · Cited by 499
A Semi-supervised Graph Attentive Network for Financial Fraud Detection
Wang, D., Lin, J., Cui, P., Jia, Q., Wang, Z., Fang, Y., Yu, Q., Zhou, J., Yang, S., & Qi, Y. (2020) · arXiv · doi:10.48550/arxiv.2003.01171 · Cited by 476
“[9] extracts some statistic features about assets, liabilities, incomes, debt and sales and uses a multi-layer feedforward network to do financial statement fraud detection.”
BackgroundMethodInfluential
Metadata
- Authors
- Ravisankar, P., Ravi, V., Raghava Rao, G., & Bose, I.
- Venue
- Decision Support Systems
- Publisher
- Elsevier BV
- Published
- 2011-01
- Volume
- 50
- Issue
- 2
- Pages
- 491-500
- ISSN
- 0167-9236 (ISSN-L)
- OpenAlex
- W2132966115
- Cited by
- 582
- References
- 2
- FWCI
- 32.64
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Detection of financial statement fraud and feature selection using data mining techniques. https://app.cochat.ai/papers/10.1016/j.dss.2010.11.006
- The paper
- Ravisankar, P., Ravi, V., Raghava Rao, G., & Bose, I. (2011). Detection of financial statement fraud and feature selection using data mining techniques. Decision Support Systems. https://doi.org/10.1016/j.dss.2010.11.006
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.
