Table 11 - uploaded by Omar Alsinglawi
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The key purpose of the research is to provide scientific data on the association between fraud causes and false financial statements. The paper gave further insight into the applicability of Altman's z-score and Dechow f-score to the exposure of false financial statements by Jordanian industrial owners. The duration of research included the years f...
Citations
... Kukreja, Mohan Gupta, Sarea, and Kumaraswamy (2020) suggest that the Z-score can reveal instances where a fictitious financial year may have been added to artificially enhance financial results and obscure potential bankruptcy risks. Additionally, the Z-score can identify potential financial manipulations and provide a broad assessment of a company's financial well-being (Mohammad et al., 2021). By analyzing various financial ratios, the Z-score can uncover hidden losses that may have accumulated over time, suggesting potential financial instability. ...
This study intends to identify the motives that lead to increasing or fighting the fraud risk in the Financial Statements (FSs) of industrial companies whose shares are traded in regulated and unregulated markets at the Amman Stock Exchange (ASE) based on the Hexagon theory, which divides the motives for fraud into six factors. The study relied on secondary data to collect and measure the study variables by extracting them from the annual reports that were published by those companies on the website of the ASE during the period of 2012–2017. The collected data were analyzed using the logistic regression model on the SPSS program. The results confirmed that the return on assets (ROA), percentage of independent members in audit committees, and tone-related party transactions had a statistically significant relationship with predicted fraudulent FSs, where these three variables belong to pressure, opportunity, and collusion fraud motives, respectively. Thus, it is worth mentioning that this study is distinguished from previous studies that examined the issue of fraud in Jordanian companies by detecting the motives of fraud according to the Fraud Hexagon theory. Moreover, some of the fraud motives were measured using new variables such as a change in inventory, the age of auditing committee’s members, and tone-related party transactions.
The COVID-19 pandemic had a wide-ranging impact, resulting in a global recession due to weakened purchasing power. This circumstance necessitates business organizations adapting to developments and being more conscious of the risk of financial statement fraud. The intention of this research is to investigate the way corporate governance affected financial statement fraud during the COVID-19 pandemic. To acquire empirical data for examining corporate governance variables on financial statement fraud, the research was examined using quantitative methods. The study takes advantage of secondary data acquired from annual reports of companies under special monitoring listed on the Indonesia Stock Exchange of 2020–2021. The logistic regression method was used to evaluate 134 data sets, and financial statement fraud was measured using the Z-Score and F-Score models. The results indicate that when using the Z-score, only the board size has a negative effect on financial statement fraud during the COVID-19 pandemic. Meanwhile, using the F-Score, the corporate governance variables studied are not proven to have an influence on financial statement fraud during the COVID-19 pandemic.
This study aims to develop a better Financial Statement Fraud (FSF) detection model by utilizing data from publicly available financial statements of firms in the MENA region. We develop an FSF model using a powerful ensemble technique, the XGBoost (eXtreme Gradient Boosting) algorithm, that helps to identify fraud in a set of sample companies drawn from the Middle East and North Africa (MENA) region. The issue of class imbalance in the dataset is addressed by applying the Synthetic Minority Oversampling Technique (SMOTE) algorithm. We use different Machine Learning techniques in Python to predict FSF, and our empirical findings show that the XGBoost algorithm outperformed the other algorithms in this study, namely, Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), AdaBoost, and Random Forest (RF). We then optimize the XGBoost algorithm to obtain the best result, with a final accuracy of 96.05% in the detection of FSF.
Bankruptcy can happen to any company, but it is very difficult to identify intentional bankruptcies that are carried out for personal gain. Currently, there is no precise methodology for identifying intentional bankruptcies, so the process depends on the skills and qualifications of the investigator. The purpose of this research is to provide a method for identifying intentional bankruptcies after examining fraud in the financial statements and their impact on the probability of bankruptcy. The paper identifies the main methods of fraud bankruptcy detection, distinguishing forensic science as the main method for doing so. The paper conducts research, which was modeled on research conducted by other authors to test the effectiveness of bankruptcy prediction methods and the effectiveness of financial indicators in detecting fraud. The research evaluated the trends of the Altman Z'-Score model and the application of binary logistic regression analysis to a sample of intentional and unintentional bankruptcies. The regression analysis provided a model for determining intentional bankruptcies and identified the following indicators: net profit/assets, liabilities/assets, liabilities/equity, and Altman Z'-Score. An independent t-test was also performed to show the differences in the means of financial ratios between intentional and unintentional bankruptcies. The results of the T-test indicated that it is important to calculate and evaluate the following additional indicators: current assets/assets, receivables/income. The results of the research may help to identify the likelihood of intentional corporate bankruptcies and thus facilitate the sophisticated methods used to date.
