Auditing with Data Analytics: Cochlear Limited Case Study Analysis

This report delves into the application of data analytics in auditing, using Cochlear Limited as a case study to illustrate real-world applications. The report begins by explaining how auditors leverage data analytics to understand a client's financial environment, emphasizing the use of trial balances, forensic analysis, and data analysis software to gain insights into business transactions and audit risks. It further highlights how data analytics assists in setting materiality misstatements and identifying fraud risk factors. The report also addresses the challenges and barriers auditors face when implementing data analytics, such as data security concerns, confidentiality issues, compatibility problems, and the high costs associated with entry for smaller audit firms. References to relevant literature are included to support the analysis. The assignment also provides an overview of the annual report, including the executive team, financial history, and the role of audit mechanisms in corporate governance.

AUDITING 1
AUDITING
By (Name)
Name of the Course
Professor
Name of the School
The City and State
Due Date

AUDITING 2
Data Analytics
Various ways can be used by auditors to use data analytics in the audit of critical risks
that organizations such as Cochlear Limited face (Tysiac 2015, p. 52). However, the auditor must
be aware of the various barriers, as well as challenges, which take place in the use of data
analytics in the audit of the company.
Data analytics helps auditors in two ways. First, it helps them to understand the financial
environment of the client, in this case, Cochlear Limited. Although it is vital to enquire from the
management about the client’s accounting system, the auditor can review the trial balance since
it is the usual source for the preparation of the financial statements (Appelbaum, Kogan and
Vasarhelyi 2017, p. 24). As such, he should obtain a year-to-year general ledger which has a
report with details of the business transactions carried out during the financial period. He should
then perform a forensic analysis to understand the flow of the transactions. He can use data
analysis software as well as the electronic version of the report to acquire substantial information
regarding the financial environment of the company and to also understand the business and
audit risks involved. The auditor should quickly review journal source to understand the type of
entries which exist, the number of manual journal entries, the affected accounts, ledger balances,
and the number of transactions included in every ledger account (Tysiac 2015, p. 52).
Secondly, data analytics helps auditors set a reasonable level of tolerable materiality
misstatement. Data analysis software does not have a limit on the amount of data it can hold for
review and, therefore, data population profiles or stratification is helpful in the identification of
individually significant items as well as groups that are significantly material. Data analytics
software also offers proper documentation which the auditor can use to base his judgment during
the planning of the audit strategy. As well, data analytics is a perfect tool for carrying out

AUDITING 3
procedures to identify fraud risk factors that are present in Cochlear. For instance, it helps the
auditor analyze and identify unexpected or unusual relationships that have been identified in
previous analytical procedures. Also, data analytics assists in the performance of disaggregated
revenue analysis either monthly, quarterly, or by product line, as well as the disaggregated
analysis of payroll and expenses (Cao, Chychyla and Stewart 2015, p. 425).
The application of data analytics in the audit of Cochlear can be faced with various
barriers as well as challenges. For instance, data security, confidentiality and compatibility are
significant challenges. It is challenging for the auditor to determine the degree to which the
extracted data can be relied upon as accurate and complete when doing the audit of Cochlear
Limited. This is because audit data analytics is carried out on the data that that been retrieved
from the entity’s system using the own software of the auditor. As a result, the two systems can
be incompatible and thus cause the extraction of inaccurate and incomplete data. Besides,
smaller audit firms face considerable barriers to entry due to the high capital, staff resources and
training needed to apply data analytics in the audit of the client (Earley 2015, p. 494).

AUDITING 4
References
Appelbaum, D., Kogan, A. and Vasarhelyi, M.A., 2017. Big Data and analytics in modern audit
engagement: Research needs. Auditing: A Journal of Practice & Theory, 36(4), pp.1-27.
Cao, M., Chychyla, R. and Stewart, T., 2015. Big Data analytics in financial statement
audits. Accounting Horizons, 29(2), pp.423-429.
Earley, C.E., 2015. Data analytics in auditing: Opportunities and challenges. Business
Horizons, 58(5), pp.493-500.
Tysiac, K., 2015. Data analytics helps auditors gain deep insight. Journal of
Accountancy, 219(4), p.52.