Big data techniques in auditing research and practice: Current trends and future opportunities

被引:135
作者
Gepp, Adrian [1 ]
Linnenluecke, Martina K. [2 ]
O'Neill, Terrence J. [1 ]
Smith, Tom [2 ]
机构
[1] Bond Univ, Bond Business Sch, Southport, Qld 4229, Australia
[2] Macquarie Univ, Fac Business & Econ, N Ryde, NSW 2109, Australia
基金
澳大利亚研究理事会;
关键词
Auditing; Big data; Data analytics; Statistical techniques;
D O I
10.1016/j.acclit.2017.05.003
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
This paper analyses the use of big data techniques in auditing, and finds that the practice is not as widespread as it is in other related fields. We first introduce contemporary big data techniques to promote understanding of their potential application. Next, we review existing research on big data in accounting and finance. In addition to auditing, our analysis shows that existing research extends across three other genealogies: financial distress modelling, financial fraud modelling, and stock market prediction and quantitative modelling. Auditing is lagging behind the other research streams in the use of valuable big data techniques. A possible explanation is that auditors are reluctant to use techniques that are far ahead of those adopted by their clients, but we refute this argument. We call for more research and a greater alignment to practice. We also outline future opportunities for auditing in the context of real-time information and in collaborative platforms and peer-to-peer marketplaces.
引用
收藏
页码:102 / 115
页数:14
相关论文
共 127 条