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  • Μικρογραφία εικόνας
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    Fraud detection & assessment of the M-score and F-score models' ability to detect financial statement fraud
    (2023-01-30) Goulandris, Nikolaos; Γουλανδρής, Νικόλαος; Athens University of Economics and Business, Department of Accounting and Finance; Doukakis, Leonidas; Siougle, Georgia; Ballas, Apostolos
    The topic of this dissertation is financial fraud detection models (Fraudulent financial reporting - FFR). More particularly traditional models like M-Score and F-Score are presented along with the Spathis (2002) model which estimates the probability of FFR. The main objective of this dissertation was the assessment of these models and which model performs better in making correct classifications. Using a sample of 136 Greek listed firms (26 FFR and 100 non-FFR) for the period 2020-2021 results showed that for FFR firms, both M-Score and F-Score failed in working properly, while F-Score adjusted with Spathis (2002) model worked completely perfect. For non-FFR firms, M-Score and F-Score models worked almost perfect, while F-Score adjusted with Spathis (2002) worked fairly good enough even little worse than traditional models. It is concluded that traditional models should incorporate updated information about the market and the period they refer in order to improve detection of FFR firms, as the wrong classification of an FFR firm as a non-FFS has long-term and more serious consequences.