ACCOUNTANT_SUMMER18_AMENDED_PG12

DATA ANALYTICS
USING DATA ANALYTICS FOR AUDIT EVIDENCE
T
echnology is shaping
and changing our lives.
Answering machines
are long gone as people feel
the need to be connected at
all times. Information needs to
be on tap as even looking up
a book takes too long. Some
employees who traditionally
worked from their desk at the
office, now work remotely
from home or even from
a different country, as staff
mobility is not even about
relocation anymore.
The audit profession is also re-inventing itself but
maybe not at the pace we’d like to believe. Whereas
auditing has been around in one form or other, for
around 3,000 years, Mautz and Sharaf started to give
shape to the theory of auditing in 1961 with their
publication of The Philosophy of Auditing. Since then,
we have seen a significant increase in guidance on
how to conduct an audit as standards were issued
by bodies such as the International Auditing and
Assurance Standards Board (IAASB) and the American
Institute of CPAs (AICPA). However, questions
continue to be raised by the public as to whether
such the bodies are doing enough to ensure that the
profession keeps with the pace of technology.
In considering whether International Standards on
Auditing (ISAs) are outdated given that they were
drafted before the availability of data analytics
techniques, during September 2016, the IAASB’s
Data Analytics Working Group (DAWG) felt the
need to issue a call for comments through its paper
entitled Exploring the Growing Use of Technology in
the Audit, with a Focus on Data Analytics. The general
consensus was that ISAs were not ‘broken or a barrier
to the application of data analytics in the audit’.
IAASB’s DAWG defines data analytics largely on the
definition used in an AICPA publication entitled Audit
Analytics and Continuous Audit - Looking Toward
the Future. The DAWG states that The quality of a
financial statement audit can be enhanced by the use
of data analytics. Data analytics, when used to obtain
audit evidence in a financial statement audit, is the
science and art of discovering and analyzing patterns,
deviations and inconsistencies, and extracting other
useful information in the data underlying or related
to the subject matter of an audit through analysis,
modelling and visualization for the purpose of
planning or performing the audit.
The use of Audit Data Analytics (ADAs) is thus seen as
an enhancement to and not as a replacement of audit
evidence. It can assist during the entire audit workflow.
In the risk assessment process, ADAs can be used
to provide better insight and support to the auditor
in identifying and assessing the risks of material
misstatement in terms of ISA 315 Identifying and
Assessing the Risks of Material Misstatement through
Understanding the Entity and Its Environment. Such
ADAs may constitute preliminary general ledger
account transaction analysis, correlation of revenue
against industry expectations and analysis of betting
behaviours in an Internet Gaming (iGaming) company.
In testing controls, an entire year’s financial
information may be used in order to:
• evaluate whether segregation of duties policies are
being followed by checking whether transactions
are authorised by an appropriate employee;
CHRISTOPHER AZZOPARDI
CHRISTOPHER AZZOPARDI IS
HEAD OF INFORMATION RISK
MANAGEMENT AT KPMG. HIS
SPECIALISATION LIES IN AUDITING
IT SYSTEMS IN SUPPORTING
FINANCIAL STATEMENTS AUDITS.
HE IS ALSO A MEMBER OF THE
MIA DIGITALISATION COMMITTEE.
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