Detecting false financial statements using published data: some evidence from Greece

By Charalambos Spathis. 2002

362 citations recorded by the source corpus.

Subfields: Accounting, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/4c46d33d9a3088b78fa190d32537af23e578b273

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In Accounting

In Management Science and Operations Research

Citations in the corpus

A Review of Financial Accounting Fraud Detection based on Data Mining Techniques (2012, neutral): “Logistic model is a generalized linear model that is used for binomial regression in which the predictor variables can be either numerical or categorical [30, 31].”
Data Mining on Finance and Accounting: a Review of Current Research Trends (2004, background): “Spathis (2002) developed two models for identifying falsified financial statement from publicly available data.”
A Review of Data Mining-Based Financial Fraud Detection Research (2007, methodology): “Detection Algorithm Reference Regression [1, 3, 5, 8, 19, 24, 26, 27] Neural networks [13, 15, 16, 18, 20, 22] Statistical tests [4, 6, 7] Bayesian [20] Decision tree [20] Stacking variant methodology [21] Others non-DM based methods [9]”
The Transition to IFRS and the Value Relevance of Financial Statements in Greece (2012, background): “…(Baralexis, 2004), very weak enforcement of accounting regulation (Baralexis, 2004; La Porta, Lopez-de-Silanes, Shleifer, & Vishny, 1998), and creative accounting is common (Baralexis, 2004; Kontos, Krambia-Kapardis, and Milonas in Jones (2011); Spathis, 2002; Spathis, Doumpos, & Zopounidis, 2002).”
MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud (2012, methodology): “The enhanced performance on manufacturing firms is consistent with previous work that has also attained good fraud detection rates on such data ( Kirkos et al. 2007; Spathis 2002).”
Identification of fraudulent financial statements using linguistic credibility analysis (2011, neutral): “[32,48]).”
The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature (2011, methodology): “be either numerical or categorical [65,84].”
Data Mining techniques for the detection of fraudulent financial statements (2007, methodology): “Spathis et al. (2002) used the UTA- DIS method to develop a falsified financial statement detection model.”