Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques

By Charalambos Spathis, Michael Doumpos, Constantin Zopounidis. 2002

207 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/f8e1c3e54d196d090dfe3d078bc535a1c4303367

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Yannis Manolopoulos
2. Efstathios Kirkos
3. Ahmed Ali Abbasi
4. Conan C. Albrecht
5. James Hansen
6. Anthony Vance
7. Dianmin Yue
8. Yunfeng Wang

In Artificial Intelligence

1. Conan C. Albrecht
2. James Hansen
3. Anthony Vance
4. Ahmed Ali Abbasi
5. Dianmin Yue
6. Yunfeng Wang
7. Yue Li
8. Chao-Hsien Chu

Citations in the corpus

• A Review of Data Mining-Based Financial Fraud Detection Research (2007, methodology): “The regression models used include logit, stepwise-logistic, multicriteria decision aid method and exponential generalized beta two (EGB2) et al. Spathis et al. [ 27 ] used a sample of 76 firms, including 38 FFS and 38 non-FFS in Greece and ten financial variables, as potential predictors of FFS.”
• Detection of financial statement fraud and feature selection using data mining techniques (2011, unknown): “Spathis et al [42] compared multi-criteria decision aids with statistical techniques such as logit and discriminant analysis in detecting fraudulent financial statements.”
• The Transition to IFRS and the Value Relevance of Financial Statements in Greece (2012, background): “, 2000; Beckman, 2007; Horton & Serafeim, 2009), looking first at those standards expected to curtail creative accounting practices (IAS 2, IAS 19, IAS 32, IAS 36, IAS 37, IAS 38 and IAS 39, as discussed above) (cf. Spathis, 2002; Spathis et al., 2002; Baralexis, 2004; Caramanis & Spathis, 2006).”
• Data Mining techniques for the detection of fraudulent financial statements (2007, methodology): “Furthermore, Spathis et al. (2002) suggest that the ratio Net Profit/Sales (NPSAL) has also proven to be significant.”
• MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud (2012, methodology): “When revenue fraud is being committed, net sales are often increased artificially and rapidly, resulting in a large AT value (Cecchini et al. 2010; Kirkos et al. 2007; Spathis 2002; Spathis et al. 2002).”
• A Review of Financial Accounting Fraud Detection based on Data Mining Techniques (2012, unknown): “In 2002, Spathis et al. [38] proposed that statistical techniques like logistic regression may be suitable to develop a model to identify factors related to fraudulent financial statement.”