ACC3600/ACF3600 Auditing and Assurance Assignment Solution

This assignment solution addresses the role of big data and data analytics in enhancing the effectiveness of external audit functions. It explores how big data can aid in predicting accounting transaction patterns, identifying red flags, and providing relevant industry insights. The solution also details how data analytics tools can be used to improve risk assessment, substantive procedures, and tests of controls, including examples such as revenue trend analysis, receivables/payables aging, and inventory management. Furthermore, it highlights the benefits of new technologies like AI and surveillance cameras in conducting more granular analysis and gathering information from non-traditional sources. References to relevant academic papers support the arguments presented.

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Auditing Theory and Practices
06 September 2018

Question 1a
Big data refers to the set of the large and the complex data that is created by the people,
applications, and smart machines; and which is beyond the capabilities of the traditional methods
meant for processing the data. Big data holds the potential of revealing a better picture of the
activities of the large entities, which cannot be captured through drawing of samples from the
population (Yoon, Hoogduin & Zhang, 2015). Some of the ways in which effectiveness of the
external audit function is enhanced using the big data are as follows.
 Big data involves prediction of information, which can prove to be an aid to the auditors
in predating the pattern of the accounting transactions by days, weeks, months, and even
years. Thus, it enables to identify the issues earlier.
 Provides the external data with high probability of detecting the red flag areas from the
wide pool of information, which the small samples often fail to represent.
 Big data involves range of information such as industry trends, real time values and more.
Thus, the evidences collected can are more relevant and can be compared with other
qualitative and quantitative information.
Data analytics refers to the set of processes namely, the extraction, inspection,
transformation and the modelling of data, in order to draw meaningful conclusions about the
information contained therein. Data analytics is a practical procedure that can aid the external
auditors in large and complex audits (Wang & Cuthbertson, 2014). It can enable the external
auditors to improve the risk assessment process, substantive procedures and tests of controls.

With the help of the data analytics tool, external auditors can filter, sort, slice and
highlight the important data (Kwon, Lee & Shin, 2014). In addition to this, the range of the
charts and diagrams can aid the auditors in the visual representation of the large data to enable
conduct of further procedures. Some of the procedures that can be effectively performed by the
auditors with the aid of the data analytics tools are as follows.
 The trends of the revenues earned can be analysed by segregating the same, either
segment wise or product wise.
 The information with respect to ageing of the receivables of the payables can be
summarised with the help of the data analytics tools.
 In case of the stock audit, data analytics can be useful to extract and summarise the
information such as the inventory ageing, level of the stock held, matching of the goods
received and dispatched and more.
 Re-computation of the complex calculation such as depreciation, on fixed assets,
segregation of the capital and the repair expenditure can be easily done.
The use of new technologies such as the artificial intelligence, surveillance cameras,
other visualization tools, and more aid the external auditors to conduct a more granular analysis
and testing of the information. The ways in which external auditors can benefit are as follows.
 Enables auditors to gather information from non-traditional sources namely the social
media, TV, radio and the internet; to gain deeper understanding of the client’s business.
 Data obtained from biometrics, sensors, voice recognition would aid the external auditors
in making the judgements (Lombardi, Bloch & Vasarhelyi, 2014).

References
Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and
acquisition intention of big data analytics. International Journal of Information Management,
34(3), 387-394.
Lombardi, D., Bloch, R., & Vasarhelyi, M. (2014). The future of audit. JISTEM-Journal of
Information Systems and Technology Management, 11(1), 21-32.
Wang, T., & Cuthbertson, R. (2014). Eight issues on audit data analytics we would like
researched. Journal of Information Systems, 29(1), 155-162.
Yoon, K., Hoogduin, L., & Zhang, L. (2015). Big Data as complementary audit evidence.
Accounting Horizons, 29(2), 431-438.