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

Spathis, Charalambos

doi:10.1108/02686900210424321

Cited by 274 publications

(365 citation statements)

References 28 publications

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“…Mitchell, 1997;Grant, 1999 andSpathis, 2002). Nevertheless, after the establishment of the Sarbanes-Oxley Act in 2002, fraud incidence still occurs.…”

Section: Resultsmentioning

confidence: 99%

Fraud Risk Assessment and Detection of Fraud: The Moderating Effect of Personality

Jaffar¹,

Hasnah²,

Iskandar³

et al. 2011

IJBM

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External auditors are required by the auditing standards to provide reasonable assurance that the financial statements are free from material misstatements. Inability of the external auditors to detect material misstatements, particularly fraud, may expose the external auditors to litigation. The present study aims to examine the moderating effect of personality factors (that are neuroticism, extraversion, conscientiousness, openness to experience and agreeableness) on the relationship between the external auditors' ability to assess fraud risk and their ability to detect the likelihood of fraud. The present study utilizes an experimental approach by sending case materials to audit partners or audit managers attached to auditing firms operating in Malaysia.The result, however, shows that none of the personality factors has moderating effect on the relationship between the external auditors' ability to assess fraud risk and their ability to detect the likelihood of fraud.

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“…Mitchell, 1997;Grant, 1999 andSpathis, 2002). Nevertheless, after the establishment of the Sarbanes-Oxley Act in 2002, fraud incidence still occurs.…”

Section: Resultsmentioning

confidence: 99%

Fraud Risk Assessment and Detection of Fraud: The Moderating Effect of Personality

Jaffar¹,

Hasnah²,

Iskandar³

et al. 2011

IJBM

View full text Add to dashboard Cite

show abstract

“…The evaluation of the financial ratios' efficiency could help to detect falsification. The ratio VAR11 that measures the efficiency of invested capital is statistically significant (p < 0.000) and is associated with the likelihood of falsification existence in (Spathis, 2002). Thus, the aim of this study is to develop a model that includes all variables.…”

Section: Categories Falsification Falsificationmentioning

confidence: 99%

“…The determining selection factors of the control sample were: a) the companies which belong to the same industry with falsified businesses and b) total assets, turnover and the number of employees are equivalent to the size of the falsified businesses (according to the published data on the Athens Exchange's website). These selected factors (accounting data) in this survey were used in many studies in the relevant worldwide literature (Spathis, 2002). The ratios-variables that help in comparing the appropriate control sample and sample of falsification in financial statements are: operating revenue, total asset, P/L for period, shareholders' funds, cash flow (see Table 1 below: VAR_1 -VAR_5).…”

Section: Sample Selectionmentioning

confidence: 99%

“…A further form of manipulation on financial statements is the intentional manipulation of expenses. According to Spathis (2002) these falsifications could cause considerable damage and have significant impact on unions, customers, investors and finally could lead the business to an economic impasse. Baralexis (2004) researched the falsifying financial statements on the part of revenue management.…”

mentioning

confidence: 99%

“…Despite the comprehensive and restrictive character of Greek law, it offers many opportunities for earnings manipulation in Greek businesses, either by exploiting IFRS weaknesses or by violating them. Furthermore, Spathis (2002) examined the effect of accounting fraud through falsification of financial statements by making an effort to evolve a disclosure falsifications model. The most common scheme that is used in falsifying financial statements includes income handling.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Detecting false financial statements: evidence from Greece in the period of economic crisis

Pazarskis

Drogalas

Baltzi

2017

Investment Management and Financial Innovations

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The purpose of this study is the examination of the financial fraud in Greek companies, listed on the Athens Exchange for the period of 2008-2015 during the economic crisis in Greece. The data of all the listed companies that were used comprise financial statements, reviews in the reports by the auditors and the figures and information based on the reports of the Athens Exchange. A total of twelve companies were found and they comprise the primary research sample with fraud in their financial statements (FFS), while another twelve companies were employed as a control sample (non-FFS) for various comparisons. From thirty financial ratios, several statistical tests to the sample and the control sample are applied in order to create a model that will use ratios as “predictors” in the analysis of financial statements for fraud. The model is accurate in classifying the total sample correctly with accuracy rates exceeding 90 percent. The results demonstrate that the model functions effectively in detecting FFS in a period of economic crisis and could be used as a tool to the banking system, from internal and external auditors and taxation or other state authorities.

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Unsupervised learning for financial statement fraud detection using manta ray foraging based convolutional neural network

Yadav

Sora

2022

Concurrency and Computation

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Summary Financial statement fraud detection is a significant topic and a challenging task related to countless applications such as financial sectors, insurance, government agencies, law enforcement and so on. This article presents an unsupervised learning model to identify the financial fraud statements using the hybrid convolutional neural network. The fraud detection scheme can be performed using a few stages: data preprocessing, sampling, feature extraction, feature selection, clustering, and classification methods. The preprocessing and sampling processes are used to clean and recognize duplicate data. Then, the proposed model analyzes both text features and financial variables to provide the best classification of financial statement fraud. In this article, a new fuzzy red deer's algorithm is proposed for selecting the optimal feature set, which can improve the classification accuracy of the fraud prediction model. Subsequently, an adaptive density based clustering (ADBC) approach is introduced for labeling the selected features through the clustering process. Finally, the fraud statements are predicted by proposing a hybrid CNN‐MRFO model wherein the hyperparameters of the CNN model are optimized through the MRFO learning algorithm. The simulation results prove the effectiveness of the proposed model in terms of accuracy (98.85%), precision (98.62%) and F‐measure (99.32%).

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

Cited by 274 publications

References 28 publications

Fraud Risk Assessment and Detection of Fraud: The Moderating Effect of Personality

Fraud Risk Assessment and Detection of Fraud: The Moderating Effect of Personality

Detecting false financial statements: evidence from Greece in the period of economic crisis

Unsupervised learning for financial statement fraud detection using manta ray foraging based convolutional neural network

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