- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
This paper analyzes 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.
5. CONCLUSION AND FUTURE OPPORTUNITIES
This paper reviews research in accounting and finance concerning data analytics and big data in order to better understand the use of big data techniques in auditing. We first point out the origins of big data techniques in the multivariate statistical literature and then categorize big data accounting and finance research under several research groupings. Our analysis shows that, in addition to auditing, there are influential papers across financial distress modelling, financial fraud modelling, and stock market prediction and quantitative modelling. We review each of these streams of research to ascertain their main contributions and to outline knowledge gaps. Unlike financial distress and financial fraud modelling, auditing has been slow to make use of big data techniques. Auditing would greatly benefit from embracing the use of big data techniques, regardless of whether client firms are using them or not. Findings from accounting and finance research suggest combining multiple big data models instead of applying an individual model, and using big data models to complement human experts.