The Transformation of Auditing From Traditional to Continuous Auditing in the Era of Big Data

Abstract

Massive usage of internet and digital devices make it easier accessing the desired information. In the past, auditing was a periodic, reactive approach, but this must change. Today, volume, velocity, variety, veracity, and value of the information, which are the main criteria of big data, are crucial. Decision makers demand timely, true, and reliable information. This need has affected every sector including auditing. For this reason, the continuous auditing system comes to debate in the big data era. The main aim of this chapter is to shed light on how traditional auditing transformed into the continuous auditing and where big data stands in this transformation. It is concluded that even though many obstacles arise, continuous auditing systems and harvesting big data benefits are crucial to gain a competitive advantage. Also, using big data analytics and continuous auditing system together, management and shareholders gain detailed information about the company's present situation and future direction.

AI-based audit of fuzzy front end innovation using ISO56002

“…The achievement of efficiency in IA is important and further research in this area is needed (Sav cuk, 2007). Emerging technologies are used to analyse a variety and volume of unstructured evidence to achieve better efficiencies in auditing (Çabuk and Aytaç, 2019;.…”

“…Auditing has caught up with this through the adoption of technology to plan and conduct audits (M. Abdolmohammadi and Usoff, 2001;Ashton, 1990;Omoteso, 2012;Sutton et al, 1995). AI is one of these technologies (Çabuk and Aytaç, 2019;Raphael, 2017;Raschke et al, 2018).…”

“…The use of AI in auditing leads to improved efficiency (Çabuk and Aytaç, 2019;Omoteso, 2012). This is achieved by the ability of AI to efficiently sift through large volumes and a variety of evidence and provide useful insights (Appelbaum et al, 2017).…”

“…The adoption of AI influences how audits are planned, conducted and reported (Ashton, 1990;Omoteso, 2012;Sutton et al, 1995). Such adoption is more complex than the adoption of simpler information technology (IT) tools due to the use of advanced underlying technologies such as machine learning or natural language processing (NLP) to make sense of the large volume, velocity and variety of evidence (Çabuk and Aytaç, 2019). It has been concluded that this complexity has so far restrained the adoption of AI in IA (Mahzan and Lymer, 2014).…”

“…(Lenz and Hahn, 2015). Being a new field, the need to study the adoption of AI for IA has been identified (Çabuk and Aytaç, 2019;Raphael, 2017). Within AI, the need for further research on the use of NLP in non-financial auditing is also identified (Fisher et al, 2016).…”