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Public auditing, analytics, and big data in the modern economy
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Appelbaum, Deniz. Public auditing, analytics, and big data in the modern economy. Retrieved from https://doi.org/doi:10.7282/T3R49TQ2

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TitlePublic auditing, analytics, and big data in the modern economy
NameAppelbaum, Deniz (author); Vasarhelyi, Miklos A (chair); Rutgers University; Graduate School - Newark
Date Created2017
Other Date2017-05 (degree)
SubjectManagement, Auditing, Big data, Quantitative research
Extent1 online resource (xiv, 340 p. : ill.)
DescriptionThere is an increasing recognition in the public audit profession that the emergence of big data as well as the growing use of analytics by audit clients has brought new concerns and opportunities. The first chapter introduces and identifies a number of these issues as that are facing the auditor in the modern economy. The second chapter primarily addresses one of these concern: what is the extant research on analytical procedures in the audit engagement? This dissertation proposes that the answers to these issues should start with an examination of the extant external audit research. However, an updated review of this research does not exist. Accordingly, 301 papers are identified regarding analytical procedures in the audit engagement. These papers are organized by technique, audit phase, and other attributes for understanding. This analysis is then presented as an External Audit Analytics (EAA) framework, which is subsequently expanded with the concepts of business analytics. Specifically, this synthesis organizes this literature, thereby offering guidelines regarding possible approaches for more complex and data driven analytics in the engagement. The third chapter elaborates and expands upon the next six issues and discusses additional aspects for contemplation by researchers and the profession. The fourth chapter discusses the issues of Big Data when it is being considered as Audit Evidence, particularly in the context of external big data. In this age of big data many sources of evidence are untraceable and their provenance unverifiable. This chapter provides guidance regarding provenance of big data, allowing it to be regarded as reliable evidence for external auditors. Finally, the fifth chapter concludes. These chapters discuss and illuminate broadly many issues facing the profession since clients are more automated and are capturing more data. These chapters also contribute to audit literature regarding external audit analytics and reliability of big data audit evidence. Big data and analytics are dramatically changing the business environment and their processes. Business methods are changing, capabilities are being added, anachronistic functions are being eliminated, and processes are being substantially accelerated. The same paradigm change should occur with the audit profession, and this dissertation provides some of the needed ideas to motivate such a shift.
NotePh.D.
NoteIncludes bibliographical references
Noteby Deniz Appelbaum
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3R49TQ2
Languageeng
CollectionGraduate School - Newark Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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