Detection Models for Unintentional Financial Restatements

Papík, Mário; Papíková, Lenka

doi:10.3846/jbem.2019.10179

Cited by 14 publications

(7 citation statements)

References 37 publications

(41 reference statements)

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“…First of all, the authors focused on fraudulent financial behaviour. Papik and Papikova (2020; tested the well-known Beneish (2001) model, on the basis of which they developed two new models based on discriminant analysis and logit regression with a predictive power of more than 70% and 60%, respectively. Svabova et al (2020) proceeded similarly, however, their model achieved up to 84% classification accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Does the life cycle affect earnings management and bankruptcy?

et al. 2021

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Research background: Deteriorating economic conditions and a negative outlook increase the pressure on financial management and the need to show high financial performance. According to Positive Accounting Theory, the growing risk of bankruptcy is associated with the phenomenon of earnings management. Bankruptcy risk and the quality of reported profits, along with other aspects of financial performance, vary throughout the company's life cycle. Nevertheless, these factors or their interactions are investigated only to a very small extent. Purpose of the article: The aim of this study is to clarify the impact of corporate life cycle and bankruptcy on earnings management, in order to describe behaviour of companies at different stages of corporate life cycle. Methods: A hierarchical mixed model with a random time and industry effect was chosen as appropriate because it allows the investigation of multilevel data that is not independent. The sample covers the financial indicators of more than 33,000 Central European companies from 2015?2019. The non-sequential Dickinson model, company age, and three models of accrual earnings management were used as proxies for the company's life cycle and quality of reported profit. Findings & value added: Earnings management and bankruptcy risk have a U-shape, indicating that financially distressed firms reduce reported accounting profit at the Introduction, Decline and, to a lesser extent, at the Growth stage. Slovak and Czech companies manipulate profits to a similar extent, Hungarian companies increase accounting profit to a greatest extent than the surveyed countries by controlling bankruptcy ? life cycle effect; however, the variability of accounting manipulations across industries has not been demonstrated. These findings imply that start-ups and declining businesses provide crooked financial statements to obtain more favourable debt covenants, and estimating discretionary accruals using life-cycle subsamples can improve the predictive power of accrual earnings management models.

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Section: Literature Reviewmentioning

confidence: 99%

Does the life cycle affect earnings management and bankruptcy?

et al. 2021

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“…where P (Y = 1∖X) is probability that company has financial accounting fraud; X k,i (for k = 1, ...., n) is an independent variable; coefficients represent predicted parameters for independent variables [54].…”

Section: Logistic Regressionmentioning

confidence: 99%

Comparing performances and effectiveness of machine learning classifiers in detecting financial accounting fraud for Turkish SMEs

Hamal¹,

Şenvar²

2021

IJCIS

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Turkish small-and medium-sized enterprises (SMEs) are exposed to fraud risks and creditor banks are facing big challenges to deal with financial accounting fraud. This study explores effectiveness of machine learning classifiers in detecting financial accounting fraud assessing financial statements of 341 Turkish SMEs from 2013 to 2017. The data are obtained from one of the leading creditor banks of Turkey. Highly imbalanced classes of 1384 nonfraudulent cases and 321 fraudulent cases (by 122 firms) are detected thus sampling techniques are used to mitigate class imbalance problem. Research methodology consists of two stages. First stage is data preprocessing wherein financial ratio calculation, feature selection methods for defining financial ratios with the greatest impact on fraudulent financial statements and two sampling methods of Synthetic Minority Oversampling Technique (SMOTE) as oversampling and undersampling are performed, respectively. Second stage is performance evaluation and comparison of classifiers wherein seven different classifiers (support vector machine, Naive Bayes, artificial neural network, K-nearest neighbor, random forest, logistic regression, and bagging) are executed and compared by using performance metrics. Classifiers are also compared without using any feature selection and/or sampling techniques. Results reveal that random forestwithout feature selection-oversampling model outperforms all other models.

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“…In fact, most financial statement fraud is implemented with the awareness or consent of management [30]. Ill-intended managers seek to manipulate earnings by committing financial statement fraud [31].…”

Section: Related Workmentioning

confidence: 99%

Detection of Financial Statement Fraud Using Deep Learning for Sustainable Development of Capital Markets under Information Asymmetry

Jan

2021

Sustainability

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Information asymmetry is everywhere in financial status, financial information, and financial reports due to agency problems and thus may seriously jeopardize the sustainability of corporate operations and the proper functioning of capital markets. In this era of big data and artificial intelligence, deep learning is being applied to many different domains. This study examines both the financial data and non-financial data of TWSE/TEPx listed companies in 2001–2019 by sampling a total of 153 companies, consisting of 51 companies reporting financial statement fraud and 102 companies not reporting financial statement fraud. Two powerful deep learning algorithms (i.e., recurrent neural network (RNN) and long short-term memory (LSTM)) are used to construct financial statement fraud detection models. The empirical results suggest that the LSTM model outperforms the RNN model in all performance indicators. The LSTM model exhibits accuracy as high as 94.88%, the most frequently used performance indicator.

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Detection Models for Unintentional Financial Restatements

Cited by 14 publications

References 37 publications

Does the life cycle affect earnings management and bankruptcy?

Does the life cycle affect earnings management and bankruptcy?

Comparing performances and effectiveness of machine learning classifiers in detecting financial accounting fraud for Turkish SMEs

Detection of Financial Statement Fraud Using Deep Learning for Sustainable Development of Capital Markets under Information Asymmetry

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