Abstract Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account. Successfully preventing this requires the detection of as many fraudsters as possible, without producing too many false alarms. This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud. In addition, classical machine learning methods must be extended, minimizing expected financial losses. Finally, fraud can only be combated systematically and economically if the risks and costs in payment channels are known. We define three models that overcome these challenges: machine learning-based fraud detection, economic optimization of machine learning results, and a risk model to predict the risk of fraud while considering countermeasures. The models were tested utilizing real data. Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15% compared to a benchmark consisting of static if-then rules. Optimizing the machine-learning model further reduces the expected losses by 52%. These results hold with a low false positive rate of 0.4%. Thus, the risk framework of the three models is viable from a business and risk perspective. 

/pdf/online-payment-fraud-from-anomaly-detection-to-risk-2yfl4ldb.pdf

Online payment fraud: from anomaly detection to risk management

Research background: In creative accounting, the primary goal of every enterprise is to increase and strengthen its market position. Over the years, manipulation of financial statements has also reached the territory of Central European countries, including the Slovak Republic. Therefore, an analysis was conducted to identify enterprises that handle accounting. This article focuses specifically on Sector A: agriculture, forestry, and fisheries.
Purpose of the article: The aim of the article was to reveal the creative accounting practices of a sample of enterprises operating in the Slovak business environment in a sector using the Beneish model.
Methods: The Beneish model was used to calculate the manipulation of enterprises? financial statements. Both variants, that is, the 5-parameter model and 8-parameter model, were used for the calculation. The results of these models were plotted using graphs and receiver operating characteristic (ROC) curves.
Findings & value added: Based on the use of both variants of the Beneish model, it was proven that enterprises in the analyzed sector use the possibility of manipulating financial statements. The added value of the article is the detection of the use of creative accounting in a specific sector, which makes the study original in its application and space-time orientation.

http://economic-research.pl/Journals/index.php/eq/article/download/2016/1915

The use of Beneish M-scores to reveal creative accounting: evidence from Slovakia

This study has the objective of examining how internal corporate governance mechanisms are related to earnings manipulation, measured by real-based earnings manipulation (REM) and accrual-based earnings manipulation (AEM). The internal corporate governance mechanisms discussed in this study signify two main kinds of board characteristics (board size and board independence) and three main kinds of ownership structure (institutional ownership, foreign ownership and business group affiliation). The study models were examined from eight countries selected from the Middle East and North Africa (MENA) region. A total of 480 non-financial firms listed between 2012 and 2019 were examined. Based on panel data-based generalised methods of moments (GMM) estimation, the findings showed that institutional ownership in MENA nations limits the use of REM and AEM. Additionally, we found that larger boards are more likely to participate in REM practices. Board independence has a positive association with REM and AEM. The results also reveal that business group affiliation has a significant effect on both methods of earnings manipulation. Foreign ownership does not seem to significantly impact either of the two manipulation methods. Finally, these results help regulators and policymakers pursue reforms to enhance national governance quality in the MENA region. 

Internal corporate governance mechanisms and earnings manipulation practices in MENA countries

This study aims to test the influence of fraud hexagon theory on financial statement fraud. Elements of the causative factors of fraud based on fraud hexagon theory cannot be directly investigated but require proxy variables. Stimulus is projected with financial stability. Opportunity is projected with nature of industry and ineffective monitoring. Rationalization is projected by a change of auditor. Capability is projected with a change of director. Arrogance is projected with a frequent number of CEO's picture. Collution is projected by the ownership of the company by the government or a State-Owned Enterprise or is a private company. The test was conducted on healthcare companies listed on the Indonesia Stock Exchange during the period 2016-2020. Sample selection is done using purposive sampling techniques. Data analysis uses regression panel data. Based on the results of the analysis showed that the variables of financial stability, ineffective monitoring, change in auditor, frequent number of CEO's picture, and state-owned enterprises positively affect financial statement fraud. Nature of industry and change of director have no effect on financial statement fraud. This research provides implications for those who need information about the possibility of financial statement fraud in healthcare companies taking into account influencing factors. 
Keyword : Fraud Hexagon Theory; Financial Statement Fraud; Healthcare Company

/pdf/determinan-fraud-hexagon-theory-dan-indikasi-financial-1q72un9q.pdf

Determinan Fraud Hexagon Theory dan Indikasi Financial Statement Fraud

Tax authorities face the challenge of effectively identifying companies that avoid paying taxes, which is not unique to European Union countries. Limited resources often constrain tax administrators, who traditionally rely on time-consuming and labour-intensive tax audit tools. As a result of this established practice, governments are losing a lot of tax revenue. The main objective of this study is to increase the efficiency of the detection of tax evasion by applying data mining methods in the East European country Lithuania, which has a rapidly developing economy, by applying data mining methods concerning affluence-related impacts. The study develops various models for segmentation, risk assessment, behavioral templates, and tax crime detection. Results show that the data mining technique can effectively detect tax evasion and extract hidden knowledge that can be used to reduce revenue losses resulting from tax evasion. This study’s methods, software, and findings can assist decision-makers, experts, and scientists in developing countries in predicting tax fraud detection.

/pdf/tax-fraud-reduction-using-analytics-in-an-east-european-7rty7d0g.pdf

Tax Fraud Reduction Using Analytics in an East European Country

Tax avoidance is one of the most frequent reasons for which companies tend to resort to creative accounting techniques. The purpose of the study is to identify which of the eight-variables from the Beneish influences the most or least the outcome of the final score, as a percent, by developing a statistical model. The sample was selected from the Bucharest Stock Exchange and consists of 66 companies traded on the main market, for the years 2015–2019. The results show that from the total of the eight variables, GMI (Gross Margin Index), AQI (Asset Quality Index), DEPI (Depreciation Index) and TATA (Total Accruals to Total Assets) are significantly influencing the probability to commit fraud. The developed model is validated with only 10% of the non-fraud companies being mistakenly considered as fraud based on our model and vice versa.

A Statistical Model of Fraud Risk in Financial Statements. Case for Romania Companies

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A Statistical Model of Fraud Risk in Financial Statements. Case for Romania Companies