Data Analytics in Auditing Cochlear: ACCG340 Case Study Analysis

This case study examines the application of data analytics in the context of a Cochlear audit, addressing the opportunities and challenges of using technology in auditing. It outlines two key ways data analytics can be implemented to enhance audit procedures and risk assessment, such as improving the effectiveness of audits and generating new forms of audit evidence. The study also identifies significant barriers to data analytics adoption, including auditors' lack of technical expertise, the limitations of small audit firms in validating financial investments, data consistency issues, and challenges related to data protection and client confidentiality. The case study highlights the importance of training auditors and adapting to technological advancements to meet the evolving expectations of stakeholders. This analysis is designed to provide a comprehensive understanding of the practical implications of data analytics in modern auditing practices, specifically within the framework of the ACCG340 course.

Running head: QUESTION 0
Auditing and Assurance in AustraliA
OCTOBER 8, 2019
STUDENT DETAILS:

QUESTION 1
Answer:
The data analytics can be implemented in auditing key risk identified for Cochlear audit in two
ways. Following are the two ways-
1. The data analytics can be applied for performing the effective audits. It could be adopted
for rendering new forms of the audit evidence.
2. Next, the data analytics can be implemented in audit procedures as well as audit planning
to recognise the risk. It can also be applied to evaluate the risks by assessing the
information to recognise correlation, fluctuation from different models along with
patterns.
The barriers that can be present in using data analytics in the audit is that auditor is not able to
proficiently and practically get information of organisation that makes them not be capable to
utilise analytics in audit. In addition, various challenges are presented in using data analytics in
audit of Cochlear. The main problem is that it is not possible for small audit firms to validate
important financial investment. They are unable to get training for using the data analytics in
audit procedure in effective way. Furthermore, the smaller audit firms may withdraw from audit
marketplace for rendering the advisory business services for the client, specifically for a client
who has elected for the audit by adopting enhanced audit exemption inceptions. Moreover, the
consistency of data rendered by clients may state challenges. There is possibility that some
controls testing would be needed to make sure that adequate, dependable and the proper audit
evidence is being made (Cao, Chychyla and Stewart, 2015).
Additionally, a level to which data recovered from the client may present the challenges for
auditors. Audit data analytics are presently being executed on data taken out from client’s system

QUESTION 2
utilising the personal software of auditor (Tysiac, 2015). The compatibility issues can take place
between different systems. In this way, the challenge would be confirming that extracted data is
correct, comprehensive, and consistent. It would also be confirming that it is not corrupted at the
time of performing data extraction procedure. There can be issues related to data protection and
client confidentiality above an extent of access provided to the auditors. In this way, the
challenges can take place in relation to keep client’s information confidential and secured. The
anti-corruption measures are essentially required to be applied to protect the integrity of data.
The utilisation of audit data analytics may develop expectation gap amongst shareholders who
state that the auditor is evaluating one hundred per cent of dealings in the particular field, so the
information of client should be completely correct (Michael and Dixon, 2019).
In this way, presently the auditor do not always operate business models in a similar manner as
the more outmoded one. The technological developments have made sophisticated systems,
which have great capabilities. In relation to this, the auditors need certain insights. It is also
required by the auditor to develop knowledge. It states the challenge that how to properly train
and instruct the upcoming auditors (Eilifsen, et. al, 2015).

QUESTION 3
References
Cao, M., Chychyla, R. and Stewart, T. (2015) Big Data analytics in financial statement
audits. Accounting Horizons, 29(2), pp.423-429.
Eilifsen, A., Kinserdal, F., Messier Jr, W.F. and McKee, T. (2019) An Exploratory Study into the
Use of Audit Data Analytics on Audit Engagements. Available at SSRN 3458485.
Michael, A. and Dixon, R. (2019) Audit data analytics of unregulated voluntary disclosures and
auditing expectations gap. International Journal of Disclosure and Governance, pp.1-18.
Tysiac, K. (2015) Data analytics helps auditors gain deep insight. Journal of
Accountancy, 219(4), p.52.