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This study aims to classify the numbers used in recent financial statement, corruption and asset misappropriation fraud schemes in such a way that these classes can be used to design effective proactive analytics-based fraud detection tests.

The data sources for the classification scheme include the court records of fraud prosecutions, investigative reports and research papers related to fraud cases.

Fraudulent numbers are most often amounts that are round, have a strong period-over-period growth, are just above or below internal control thresholds or other targets, are deviations from Benford’s Law, are purposeful duplicates of authentic transactions, are outliers due to being excessively large and are excessively rounded up or down. The study includes several examples of fraudulent numbers.

The fraudulent number types are based on a sample of fraud-related court documents, and the sample might not be representative of the population of detected and undetected frauds. Further research is needed into the detection of corruption/bribery schemes.

The results are important for auditors and forensic accountants running proactive fraud detection tests. The discussions emphasize that the analysis should include refining and rerunning the tests, and then using groupings and filtering to deal with false positives. The importance of an effective audit of the notable transactions is stressed in the concluding section.

The study is an original in-depth coverage of the patterns found in fraudulent numbers. The discussion sections review implementation issues and considerations for future research.

In 2001 Enron filed amended financial statements setting off a chain of events starting with its bankruptcy filing and including the conviction of Arthur Andersen for obstruction of justice. The end of 2001 and the first half of 2002 included a heightened level of publicity for the accounting practices of listed companies. This paper addresses whether there was a detectable change in the incidence of earnings management around this time period. Earnings reports released in 2001 and 2002 were analyzed. The results showed that revenue numbers were subject to upwards management. Benford's Law was used to detect such manipulations. Earnings Per Share (EPS) numbers showed a marked discontinuity in the distribution around zero which is consistent with upwards management. The results also showed a tendency towards neat round EPS numbers such as 0.10, 0.20, etc. The overall results are consistent with a small but noticeable increase in earnings management in 2002. Enron's reported numbers are reviewed and these show a strong tendency towards making financial thresholds.

This paper develops a vector variation score that quantifies the change in an array of data points from period-to-period. The array could be the amounts reported on an income tax return, the closing stock prices for a set of listed companies, the monthly sales amounts for retail locations or the monthly balances in general ledger accounts.

The score is grounded in analytic geometry. The angle θ measures whether the changes were uniformly spread across the line items. The item(s) with the largest contribution(s) to the score can be identified. Line items can be weighted such that they contribute less than fully to the score.

The method can identify tax returns with large year-on-year changes. The method can identify the fact that the price movements during earnings season are less dependent than is usually the case. The method can identify anomalies in reported sales amounts. The method should be able to identify ledger accounts’ large abnormal changes.

Auditors will need to be trained to interpret the results and to reduce the number of false positives.

The score could be used in both external and internal audit applications where auditors want to quantify and rank period-on-period changes in a search for outliers.

The change score is normalized to the [0, 1] range. The results can be plotted as a polar plot for display on an auditing dashboard. The contribution of a single line item can be calculated and line items can be weighted to prevent them from having an undue influence on the results.

Benford's Law is an empirical observation about the frequency of digits in a variety of naturally occurring data sets. Auditors and forensic scientists have used Benford's Law to detect erroneous data in accounting and legal usage. One well-known limitation is that Benford's Law fails when data have clear minimum and maximum values. Many kinds of education data, including assessment scores, typically include hard maximums and therefore do not meet the parametric assumptions of Benford's Law. This paper implements a transformation procedure which allows for assessment data to be compared to Benford's Law. As a case study, a data quality assessment of oral language scores from the Early Childhood Longitudinal Study, Kindergarten (ECLS-K) study is used and higher risk data segments detected. The same method could be used to evaluate other concerns, such as test fraud, or other bounded datasets.

The purpose of this paper is to understand if forensic accounting techniques and tools could contribute to the deterrence of fraud in financial statements, considering the expertise of forensic accountant on ex post activities and that the traditional mechanisms to prevent this type of fraud have not been sufficient to stop the impact on companies, investors, auditors, employees and on society in general.

This research was carried out using a qualitative exploratory study with a phenomenological approach conducted through in-depth interviews with professional experts in the forensic field.

The findings confirm that the use of forensic accounting techniques and tools could contribute to the prevention of fraud in financial reporting not only when the risk of fraud has been materialized. Similar studies, about fraud prevention addressing the situation under a qualitative approach from the perspectives of its protagonists, have not been observed in the bibliographical review, so this research contributes to expanding the scientific research, the study and practice of forensic accounting.

From a business management perspective, this study contributes a paradigm shift from the traditional ex post forensic auditing activity toward an ex ante activity to improve management control systems within organizations anywhere in the world. Because this study is guided to prevent fraudulent financial statements, other fraud categories such as misappropriation or corruption could be addressed in other studies and various countries.

Introduces a new analytical review procedure that measures the degree to which a data set’s digit distribution deviates from a Benford digit distribution. This deviation can indicate potential manipulation and can be used to signal the need for further audit testing. An artificial neural network is used to distinguish between “normal” and “manipulated” financial data. The results show that if data have been contaminated (at a 10 per cent level or more) a Benford analytical review procedure will detect this 68 per cent of the time. If the data are not contaminated, the test will indicate that the data are “clean” 67 per cent of the time. Because analytical review procedures are not used in isolation, these results probably understate the effectiveness and potential of a digits‐based analytical review procedure. This procedure’s fraud detection results compare favorably to traditional analytical review procedures. Importantly, its unique analysis procedure allows it to complement traditional analytical review procedures. A key limitation of this study is that it uses simulated data, rather than actual data. Such an enhancement will be a critical step in future research. This method appears to have potential merit and provides many opportunities for new research.

This study aims to objectively synthesize the volume of accounting literature on financial statement fraud (FSF) using a systematic literature review research method (SLRRM). This paper analyzes the vast FSF literature based on inclusion and exclusion criteria. These criteria filter articles that are present in the accounting fraud domain and are published in peer-reviewed quality journals based on Australian Business Deans Council (ABDC) journal ranking. Lastly, a reverse search, analyzing the articles' abstracts, further narrows the search to 88 peer-reviewed articles. After examining these 88 articles, the results imply that the current literature is shifting from traditional statistical approaches towards computational methods, specifically machine learning (ML), for predicting and detecting FSF. This evolution of the literature is influenced by the impact of micro and macro variables on FSF and the inadequacy of audit procedures to detect red flags of fraud. The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper chronicles the cluster of narratives surrounding the inadequacy of current accounting and auditing practices in preventing and detecting Financial Statement Fraud. The primary objective of this study is to objectively synthesize the volume of accounting literature on financial statement fraud. More specifically, this study will conduct a systematic literature review (SLR) to examine the evolution of financial statement fraud research and the emergence of new computational techniques to detect fraud in the accounting and finance literature.

The storyline of this study illustrates how the literature has evolved from conventional fraud detection mechanisms to computational techniques such as artificial intelligence (AI) and machine learning (ML). The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper contributes to the literature by providing insights to researchers about why the evolution of accounting fraud literature from traditional statistical methods to machine learning algorithms in fraud detection and prediction.

The purpose of this paper is to formulate and propose a fraud investigation plan that forensic accountants can use to investigate financial frauds. In particular, the paper sets out the structure and rationale of the fraud investigation plan that both forensic accountants and fraud examiners can use in their investigation of false accounting and theft charges.

The paper uses the material facts from the Polly Peck International fraud as a prototype case upon which to build an investigation plan and detail potential areas of investigation to establish evidence for a criminal trial.

The findings revealed that the case can be used to provide insights on evidence gathering techniques and test particular models of fraud detection. The concealment and conversion evidence gathering techniques provide fodder on how to gather and triangulate both direct and circumstantial evidence that can be used to avoid mistrials in courts.

The case is of interest to practitioners and forensic and fraud examination students who would like to build on their existing knowledge and obtain insights into the steps to follow to conduct an investigation and gather evidence to build a case. The paper makes specific recommendations to enhance the effectiveness and efficiency of investigations.

The paper is among one of the few to propose a fraud investigation plan designed to investigate cases involving false accounting and theft charges. More importantly, the paper uses a real case to illustrate how to examine documentation/data and how such documentation will be analysed in a trial.

The purpose of this paper is to review, critique, and integrate certain trends, events, and research streams involving earnings management, fraudulent financial reporting, corporate governance and ethics.

The paper provides a brief history of relevant events and trends in financial reporting for the period 1987‐2007. Within this historical context, financial reporting and earnings quality are discussed from the academic and practitioner points of view. The influence of corporate governance and the role of ethics and behavior are introduced as part of an integrated discussion of academic and practitioner viewpoints of earnings management and fraudulent financial reporting. The last section of the paper provides final observations and recommendations for future research.

The paper concludes that academic research in earnings management and fraudulent financial reporting has become increasingly narrow in addressing important issues and problems in practice.

The paper is limited in its depth of analysis in each individual research stream due to the breadth of research and time period that are addressed. The implications for future research are enhanced by the integration of several streams of research relevant to earnings management and fraudulent financial reporting.

The paper may be useful to regulators and policy makers to better understand the significance and relevance of academic research.

The paper introduces and integrates ethics and behavior as important aspects for understanding earnings management and fraudulent financial reporting.