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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.

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 study was to investigate how pension funds use charts in popular reports. Popular reports communicate a fund’s financial health to non-technical audiences, and often contain charts, tables, and other graphical elements. Do these graphics meet audiences’ information needs and align with chart best practices?

This study focused on the 60 retirement funds receiving a 2021 popular report award from the Government Finance Officers Association. The author analyzed each graphic’s topic and design.

Most funds presented key topics (such as funding rate and portfolio return), but they generally lacked helpful benchmarks or peer comparisons. A total of 30% of reports had one or more broken charts, where their visual elements did not match the underlying data. A total of 70% of the reports contained at least one badly designed chart. These design flaws included non-zero (truncated) axes, hidden non-zero axes and misleading 3D perspectives.

To the best of the authors’ knowledge, this paper is the first to examine chart quality in pension fund popular reports.