تکنیک های کلان داده در حسابرسی تحقیقات و رفتار Big data techniques in auditing research and practice: Current trends and future opportunities

1. INTRODUCTION 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 and their origins in the multivariate statistical literature to help unfamiliar auditors understand the techniques. We then review existing research on big data in accounting and finance to ascertain the state of the field. Our analysis shows that – in addition to auditing – existing research on big data in accounting and finance extends across three other genealogies: (1) financial distress modelling, (2) financial fraud modelling, and (3) stock market prediction and quantitative modelling. Compared to the other three research streams, auditing is lagging behind in the use of valuable big data techniques. Anecdotal evidence from audit partners indicates that some leading firms have started to adopt big data techniques in practice; nevertheless, our literature review reveals a general consensus that big data is underutilized in auditing. A possible explanation for this trend is that auditors are ACCEPTED MANUSCRIPT 4 reluctant to use techniques and technology that are far ahead of those adopted by their client firms (Alles, 2015). Nonetheless, the lack of progress in implementing big data techniques into auditing practice remains surprising, given that early use of random sampling auditing techniques put auditors well ahead of the practices of their client firms. This paper contributes to bridging the gap between audit research and practice in the area of big data. We make the important point that big data techniques can be a valuable addition to the audit profession, in particular when rigorous analytical procedures are combined with audit techniques and expert judgement. Other papers have looked at the implications of clients’ growing use of big data (Appelbaum, Kogan, & Vasarhelyi, in press) and the sources of useful big data for auditing (e.g., Vasarhelyi, Kogan, and Tuttle (2015); Zhang et al. (2015)); our work focuses more on valuable opportunities to use contemporary big data techniques in auditing. We contribute to three research questions regarding the use of big data in auditing, raised by Appelbaum et al. (in press) and Vasarhelyi et al. (2015): “What models can be used?”, “Which of these methods are the most promising?” and “What will be the algorithms of prioritization?” We provide key information about the main big data techniques to assist researchers and practitioners understand when to apply them.