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This paper aims to examine the relevance of Big Data to forensic accounting practice and education by gathering opinions from a sample of academics and practitioners in China.

The authors conduct a survey of academics and practitioners regarding the desired demand, importance and content of Big Data educational skills and topics for forensic accounting education to effectively respond to challenges and opportunities in the age of Big Data.

Results indicate that the demand for and interest in Big Data/data analytics and forensic accounting will continue to increase; Big Data/data analytics and forensic accounting should be integrated into the business curriculum; many of the suggested Big Data topics should be integrated into forensic accounting education; and some attributes and techniques of Big Data are important in improving forensic accounting education and practice.

Readers should interpret the results with caution because of the sample size (95 academics and 103 practitioners) and responses obtained from academics and practitioners in one country (China) that may not be representative of the global population.

The results are useful in integrating Big Data topics into the forensic accounting curriculum and in redesigning the forensic accounting courses/programs.

The results have implications for forensic accountants in effectively fulfilling their responsibilities to their profession and society by combating fraud.

This study provides educational, research and practical implications as Big Data and forensic accounting are advancing.

Professionals who carry out the forensic accounting profession must have an extensive knowledge of accounting, as well as an effective knowledge of law, auditing, internal audit, business management, psychology, crime science, and, in particular, computer technologies. In today’s digital business environment, it has become difficult to identify fraudulent transactions with traditional methods. Developments in information (data) and information technology have helped increase anti-fraud control programs and fraud research opportunities. In particular, fraudulent financial reporting disrupts the reliability, accuracy, and efficiency of financial markets in terms of existence and continuity. The forensic accounting profession has been able to improve the effectiveness of inspections by using big data techniques, data analytics, and algorithms (Rezaee, Lo, Ha, & Suen, 2016; Seda & Kramer, 2014; Singleton & Singleton, 2010).

The aim of the author, in this chapter, is to evaluate the contribution of using big data techniques in forensic accounting applications and the skills that will be provided to students while integrating these techniques in forensic accounting trainings. For this purpose, studies on forensic accounting education and their applications were reviewed. In addition, opinions were evaluated by considering the relevant literature about the importance of big data, benefits of big data, use of big data techniques, and interest shown of them.

Accounting education is re-inventing itself as technology impacts the practical aspects of accounting in the real world and education tries to keep up. Big Data and data analytics have begun to influence elements of accounting including audit, accounting preparation, forensic accounting and general accountancy consulting. The purpose of this paper is to qualitatively analyse the current skills provision in accounting Masters courses linked to data analytics compared to academic and professional expectations of the same.

The academic expectations and requirements of the profession, related to the impact of Big Data and data analytics on accounting education were reviewed and compared to the current provisions of this accounting education in the form of Masters programmes. The research uses an exploratory, qualitative approach with thematic analysis.

Four themes were identified of the skills required for the effective use of Big Data and data analytics. These were: questioning and scepticism; critical thinking skills; understanding and ability to analyse and communicating results. Questioning and scepticism, as well as understanding and ability to analyse, were frequently cited explicitly as elements for assessment in various forms of accounting education in the Masters courses. However, critical thinking and communication skills were less explicitly cited in these accounting education programmes.

The research reviewed and compared current academic literature and the requirements of the professional accounting bodies with Masters programmes in accounting and data analytics. The research identified key themes relevant to the accounting profession that should be explicitly developed and assessed within accounting education for Big Data and data analytics at both university and professional levels. Further analysis of the in-depth curricula, as opposed to the explicitly stated topic coverage, could add to this body of research.

This paper considers the potential combined role of professional qualification examinations and master’s degrees in skills provision for future practitioners in accounting and data analysis. This can be used to identify the areas in which accounting education can be further enhanced by focus or explicit mention of skills that are both developed and assessed within these programmes.

The paper considers the interaction between academic and professional practice in the areas of accounting education, highlighting skills and areas for development for students currently considering accounting education and data analytics.

While current literature focusses on integrating data analysis into existing accounting and finance curricula, this paper considers the role of professional qualification examinations with Masters degrees as skills provision for future practitioners in accounting and data analysis.

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.

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 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.

The aim of the study is to identify the latest trends in accounting forensic work in Australia by examining how accounting firms that specialise in forensic services meet the needs of their clients, and to inform universities on the appropriate curricula to ensure the knowledge and skills of future graduates meet industry expectations.

The methodological approach taken in this study was exploratory, and qualitative semi‐structured interviews were the primary data collection instrument used.

Findings from 32 interviews with Australian practising forensic professionals suggest that these services are broad and complex. Opinions differ widely on the best way forward for this area of the accounting profession. Both work‐based and personal attributes required by practising forensic professionals together with the wide range of complex services offered in Australia are presented in a posited model, providing a unique contribution to international forensic accounting literature. Forensic services firms require strong work‐based skills such as oral and written communication skills, technology and analytical skills, in addition to an accounting qualification, as part of their under‐graduate or post‐graduate degrees.

Perceptions were also that graduates require strong interpersonal skills, enthusiasm, intelligence and the ability to work independently and although this has been reported in the literature previously, findings from this study suggest there is still a deficiency in forensic accounting graduates skill set, particularly in relation to oral and written communication. The lack of an Australian‐based forensic accounting certification was also raised.

Both work‐based skills and personal attributes are presented in a posited model of the Australian forensic accountant, providing a unique contribution to international forensic accounting literature.

For over a decade now, various stakeholders in accounting education have called for the integration of technology competencies in the accounting curriculum (Association to Advance Collegiate Schools of Business (AACSB), 2013, 2018; Accounting Education Change Commission (AECC), 1990; American Institute of Certified Public Accountant (AICPA), 1996; Behn et al., 2012; Lawson et al., 2014; PricewaterhouseCoopers (PWC), 2013). In addition to stakeholder expectations, the inclusion of data analytics as a key area in both the business and accounting accreditation standards of the AACSB signals the urgent need for accounting programs to incorporate data analytics into their accounting curricula. This paper examines the extent of the integration of data analytics in the curricula of accounting programs with separate accounting AACSB accreditation. The paper also identifies possible barriers to integrating data analytics into the accounting curriculum. The results of this study indicate that of the 177 AACSB-accredited accounting programs, 79 (44.6%) offer data analytics courses at either the undergraduate or graduate level or as a special track. The results also indicate that 41 (23.16%) offer data analytics courses in their undergraduate curriculum, 61 (35.88%) at the graduate level, and 12 (6.80%) offer specialized tracks for accounting data analytics. Taken together, the findings indicate an encouraging trend, albeit slow, toward the integration of data analytics into the accounting curriculum.

The purpose of this paper is to demonstrate the technical feasibility of implementing multi-view visualization methods to assist auditors in reviewing the integrity of high-volume accounting transactions. Modern enterprise resource planning (ERP) systems record several thousands of transactions daily. This makes it difficult to find a few instances of anomalous activities among legitimate transactions. Although continuous auditing and continuous monitoring systems perform substantial analytics, they often produce lengthy reports that require painstaking post-analysis. Approaches that reduce the burden of excessive information are more likely to contribute to the overall effectiveness of the audit process. The authors address this issue by designing and testing the use of visualization methods to present information graphically, to assist auditors in detecting anomalous and potentially fraudulent accounts payable transactions. The strength of the authors ' approach is its capacity for discovery and recognition of new and unexpected insights.

Data were obtained from the SAP enterprise (ERP) system of a real-world organization. A framework for performing visual analytics was developed and applied to the data to determine its usefulness and effectiveness in identifying anomalous activities.

The paper provides valuable insights into understanding the use of different types of visualizations to effectively identify anomalous activities.

Because this study emphasizes asset misappropriation, generalizing these findings to other categories of fraud, such as accounts receivable, must be made with caution.

This paper provides a framework for developing an automated visualization solution which may have implications in practice.

This paper demonstrates the need to understand the effectiveness of visualizations in detecting accounting fraud. This is directly applicable to organizations investigating methods of improving fraud detection in their ERP systems.

The awareness of probability was observed in ancient cultures through the discovery of primitive dice games made with animal bones. The history of analytics in the workplace, as it is currently known (defined as predictive analytics), probably started in ancient Roman times, when the concept of insurance was first created. While the previous example showed that analytics for business had been around for some time, it is only relatively recently that there is an increased emphasis on the use of analytics in the modern firm. Credit card firms and retail catalog companies relied on analytics to drive their business models, for most of the latter half of the twentieth century. The use of advanced analytics for business also grew around the Millennium since the widespread use of data warehousing and relational databases on client servers. Moreover, Machine Learning and Artificial Intelligence Techniques, which have been around for many decades, have had very few breakthrough successful applications up until recently when cloud computing and being able to take advantage of the infrastructure of companies, such as Amazon and Google, with their Cloud Services enabled these algorithms to be used to their full extent in firms. This powerful infrastructure availability coupled with BIG DATA is creating breakthrough applications across many business models on a consistent basis. This chapter explores the use of advanced analytics across different business functional areas. It also introduces some breakthrough models, which include Netflix, Pandora, eHarmony, Zillow, and Amazon, and explores how these are not only changing the lives of consumers but also changing the nature of the workplace and creating new issues for firms such as data protection and liabilities for the actions of automated algorithms.