Big Data and Analytics in the Modern Audit Engagement: Research Needs

被引:182
作者
Appelbaum, Deniz [1 ]
Kogan, Alexander [2 ]
Vasarhelyi, Miklos A. [2 ]
机构
[1] Montclair State Univ, Montclair, NJ 07043 USA
[2] Rutgers State Univ, Newark, NJ USA
来源
AUDITING-A JOURNAL OF PRACTICE & THEORY | 2017年 / 36卷 / 04期
关键词
audit analytics; Big Data; external audit; audit evidence; SUBJECTIVE-PROBABILITY ELICITATION; DEMPSTER-SHAFER THEORY; CURRENT STATE; RISK; INFORMATION; BELIEF; FRAUD; SYSTEMS; OPPORTUNITIES; PERSPECTIVE;
D O I
10.2308/ajpt-51684
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
Modern audit engagements often involve examination of clients that are using Big Data and analytics to remain competitive and relevant in today's business environment. Client systems now are integrated with the cloud, the Internet of Things, and external data sources such as social media. Furthermore, many engagement clients are now integrating this Big Data with new and complex business analytical approaches to generate intelligence for decision making. This scenario provides almost limitless opportunities and the urgency for the external auditor to utilize advanced analytics. This paper first positions the need for the external audit profession to move toward Big Data and audit analytics. It then reviews the regulations regarding audit evidence and analytical procedures, in contrast to the emerging environment of Big Data and advanced analytics. In a Big Data environment, the audit profession has the potential to undertake more advanced predictive and prescriptive-oriented analytics. The next section proposes and discusses six key research questions and ideas, followed with emphasis on the research needs of quantification of measurement and reporting. This paper provides a synthesis and review of the concerns facing the audit community with the growing use of Big Data and complex analytics by their clients. It contributes to the literature by expanding upon these emerging concerns and providing opportunities for future research.
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页码:1 / 27
页数:27
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