Data Analysis for Audit Training (2016.06)

www.hanrickcurran.com.au
Data Analysis for Hanrick Curran
June 2016

2experience. new thinking
What can you learn …
 Basic data analysis requirements
 Using Excel™
 Using TeamMate™
 Audit implications

4experience. new thinkingexperience. new thinking 4
An understanding of the basics of performing
data analysis and computer assisted audit
techniques (“CAATs”) is a core part of any
auditors toolkit.
Basics

Terminology
 Workbook – an excel file with multiple sheets
 Worksheet – an individual sheet in an excel file
 Row (record) – a row of data on a worksheet
 Column (field) – a column of data on a worksheet
 Table – a part of a worksheet containing data
 Array –
 Control totals – details of the file to be imported that
provide certainty that all data has been received / imported

Audit benefits
 Data analysis adds to the evidence that we obtain
during our audit procedures by enabling us to look
at data for the entire population.
 This analysis provides additional evidence regarding
our audit that is supplemental to our detailed
substantive testing.
 Data analysis may provide anywhere from minimal
to persuasive evidence for the purpose of “Other
Substantive Tests”. Of course, this assessment
remains a matter of professional judgement.

Audit benefits

Getting data from the client
 Plan your request to the client
 Set out your needs clearly (email / letter)
 Allow plenty of time
 Include a diary reminder

Control totals
 When requesting data from clients, have them
confirm control totals
 Control totals are usually details such as the
number of rows / records and the value of a
control field (e.g., total payments)
 Use the control total to confirm that all records
have been correctly imported and that the
value of transactions imported agrees with the
information provided by the client

Protecting evidence
 When analysing data in excel, be careful to
maintain the integrity of the information
supplied by the client
 Work on a copy of the client’s worksheet
 Keep the original client datafile separate
from the working file
 Do your backups

Audit documentation (i)
 A quote from ASA 230

Audit documentation (ii)
 You need to retain sufficient evidence in
your audit workpapers to meet the
standards
 This does not mean you have to keep the
full client records (e.g., payments register)

Excel can be used for a large amount of basic
data analysis
Excel analysis

Sort, Filter & Subtotal
 Much can be
achieved with simple
functions of sort,
filter and subtotals

Pivot tables
 Pivot tables provide
an even more
powerful way to
analyse date in excel

Vlookup – to join data tables
 Vlookup can be used to join data tables
 Key is to have a common data field (“key”)
that is in both tables
 Activity – vlookup on bank account
between payroll list and AP list

Merge v centre formatting of cells
 These look similar, but behave differently
when a column is selected (CTRL +
Spacebar)
 When using “centre across” (in CTRL + 1)
you can more easily select only one column.

Screen clippings
 Use the screen clipping button from the
Quick Access Toolbar to capture screen
shots for documenting audit testing

Data links
 Links between
workbooks are to be
avoided if possible
 Use the Data / Edit
Links menu to
manage links

TB editing
 TB’s come in many formats, often with the
“-” or credit sign at the back of numbers.
 Activity– for TB(1) and TB(2), use the
functions to manipulate the data to
prepare a TB for importing to CaseWare

TB editing - example
Steps to fix the TB
 Use TeamMate / ASAP-Utilities ? (see http://www.asap-utilities.com/)
 Sort the TB on the balance numbers
 In the next column over, copy the numbers into the new column,
leave the debit numbers alone
 For the credit numbers, include a formula “= - (main number)”
 On the main numbers, highlight the credit numbers and use CTRL + H
to access “find and replace”
 Find the minus sign (-) and replace with nothing
 For the new column of numbers (which should now add to zero), copy
the column and use paste values (ALT-H-V-V) paste the values into
the sheet
 Delete the column with the original numbers and import to CaseWare
TB module

TeamMate is the data analysis software that we
have selected for our auditors. This software
works as an add-in to Excel and should be used
as the basis for our data analysis during audit
assignments.
We have other tools available, which include
IDEA and Spreadsheet Detective.
TeamMate

Append / join sheets
 Join sheets - Joins two sheets together, based
on a common matching column, e.g. common
bank test, inventory for NPV testing. (Activity)
 Append sheets - Joins multiple sheets that all
have the same column structure together. For
example, where you have separate reports for
each sales person, each warehouse, etc. that
you wish to combine into one report. (Activity)

Manipulate fields
 An available tool inside TeamMate that can
be used for data manipulation
 Can be used for all sorts of different actions
and is well worth exploring
 Highlights :
 Normalize fields – tool to standardize fields
 Debit and credit columns – splits/ combines Dr and Cr columns
(example)
 Merge wrap and autofit – combines those functions from Excel into
one (example)

Stratify
 Used to obtain information regarding the
characteristics of a data set
 Should be run on all files as a preliminary
activity with the objective of understanding
the nature of the population under
investigation
 Activity – stratify the expenses listing

Extract (duplicates / gaps / sample)
 Can be used to extract relevant data from a population
(e.g., duplicates, gaps or samples)
 Gaps extraction - Identifies for gaps in a sequence of
numbers or partly numeric references, such as missing
invoices or journals.
 Duplicates extraction - Extracts duplicate records, based on
up to 3 fields you specify.
 Can be used to isolate specific transactions/records for
further investigation
 Activity – review the Payables for duplicates, extract gaps in
General journal listing

Benford's Test
 Based on the work of Frank Benford

Benford's Test
 Benford’s test should be run on all data sets
to identify any anomalous transactions that
may warrant further investigation
 Activity – run Benford’s on the expenses
listing

Outliers
 Used to extract records from a data set that
are at the edge of the normal distribution
 Can be used to extract records more than X
times the average or standard deviation

Sampling
 Sampling is used to extract records for
further analysis as part of detailed
substantive testing
 Various types of sampling can be
undertaken (refer additional slides)

Sampling - MUS
 Our preferred method for sample selection
is the use of a MUS (Monetary Unit
Sample).
 A MUS sample is preferred because of the
effectiveness of the audit testing that
results and the efficiency of the sample
selection (e.g., all ‘high value’ items will be
selected).

Sampling MUS – activity
 Using materiality of $4,000 generate a
MUS sample for the expenditure file

 Random sample - Extracts a number of randomly
sampled items from your population.
 Systematic sample - Extracts every ‘nth’ item from
the population.
 Stratified sample – Stratifies data and select a
random sample from each strata band
Other types of sampling

 Activity on Revenue analytics
Year-on-year analytical review with
TeamMate

If you want to explore more, visit video
demonstrations website:
http://www.topcaats.com/resources/demonst
rations/

AUDIT EVIDENCE
Understanding the requirements of ASA 500

ASA 500 – audit evidence
 4. The objective of the auditor is to design
and perform audit procedures in such a way
as to enable the auditor to obtain sufficient
appropriate audit evidence to be able to
draw reasonable conclusions on which to
base the auditor’s opinion.
Audit Training -

 5(b). Appropriateness means the measure of
the quality of audit evidence; that is, its
relevance and its reliability in providing support
for the conclusions on which the auditor’s opinion
is based.
 5(e) Sufficiency means the measure of the
quantity of audit evidence. The quantity of the
audit evidence is affected by the auditor’s
assessment of the risks of material misstatement
and also by the quality of such audit evidence.
Audit Training -

9. When using information produced by the entity, the
auditor shall evaluate whether the information is
sufficiently reliable for the auditor’s purposes,
including as necessary in the circumstances:
(a) Obtaining audit evidence about the accuracy and
completeness of the information; and
(b) Evaluating whether the information is sufficiently
precise and detailed for the auditor’s purposes.
“ASIC’s Top 10 issues for auditors includes a failure to apply professional
scepticism and document work.
Audit Training -

ASA 500 – Requirements for audit
evidence
11. if:
(a) audit evidence obtained from one source is
inconsistent with that obtained from another;
or
(b) the auditor has doubts over the reliability of
information to be used as audit evidence,
the auditor shall determine what modifications
or additional to audit procedures are necessary to
resolve the matter, and shall consider the effect
of the matter, if any, on other aspects of the
audit.
Audit Training -

evidence
 A2. … procedures to obtain audit evidence
can include
 Inspection
 Observation
 Confirmation
 Re-calculation
 Re-performance
 Analytical procedures
 Enquiry
Audit Training -

evidence
 A52. … The means available to the
auditor for selecting items for testing are:
(a) selecting all items (100% testing)
(b) selecting specific items
(c) audit sampling
Testing
All items
Specific items
Sampling
Audit Training -

AUDIT SAMPLING
Understanding the requirements of ASA 530

sampling
 5(a). Audit sampling means the application of
audit procedures to less than 100% of items
within a population of audit relevance such that
all sampling units have a chance of selection in
order to provide the auditor with a reasonable
basis on which to draw conclusions about the
entire population.
 5(b) Population means the entire set of data
from which a sample is selected and about which
the auditor wishes to draw conclusions.
Audit Training -

sampling
 5(c). Sampling risk means the risk that the
auditor’s conclusions based on a sample may
be different from the conclusion if the entire
population were subjected to the same audit
procedure.
 5(d) Non-sampling risk means the risk that
the auditor reaches an erroneous conclusion
for any reason not related to sampling risk.
Audit Training -

sampling
 5(e). Anomaly means a misstatement or
deviation that is demonstrably not
representative of misstatements or
deviations in a population.

Audit Training -

sampling
 5(g). Statistical sampling means an
approach to sampling that has the following
characteristics:
(i)Random selection of sample items; and
(ii)The use of probability theory to evaluate
sample results, including measurement of
sampling risks.
A sampling approach that does not have
characteristics (i) and (ii) is considered non-
statistical sampling.
Audit Training -

sampling
 5(i). Tolerable misstatement means a
monetary amount set by the auditor in respect of
which the auditor seeks to obtain an appropriate
level of assurance that the monetary amount set
by the auditor is not exceeded by the actual
misstatement in the population.
“Typically this would be the amount
determined as materiality, specifically the
performance materiality, or a lower amount.
Audit Training -

sampling
12. The auditor shall investigate the nature and cause of any deviations or
misstatements identified, and evaluate their possible effect on the purpose of the
audit procedure and on other areas of the audit
13. In the extremely rare circumstances when the auditor considers a misstatement or
deviation discovered in a sample to be an anomaly, the auditor shall obtain a high
degree of certainty that such misstatement or deviation is not representative of the
population. The auditor shall obtain this degree of certainty by performing
additional audit procedures to obtain sufficient appropriate evidence that the
misstatement or deviation does not affect the remainder of the population.
Audit Training -

Sample selection methods (ASA 530.App
4)
(a) Random selection (applied through random number generators or random number
tables)
Audit Training -

4)
 (b) Systematic selection, in which the number of sampling units in the
population is divided by the sample size to give a sampling interval, for example 50,
and having determined a starting point within the first 50, each 50th sampling unit
thereafter is selected. Although the starting point may be determined haphazardly,
the sample is more likely to be truly random if it is determined by use of a
computerised random number generator or random number tables. When using
systematic selection, the auditor would need to determine that sampling units within
the population are not structured in such a way that the sampling interval
corresponds with a particular pattern in the population.
Audit Training -

4)
(c) Monetary Unit Sampling is a type of value-weighted selection (as described in
Appendix 1) in which sample size, selection and evaluation results in a conclusion in
monetary amounts.
 “Also known as Dollar Unit Sampling (DUS) or Constant Monetary
Amount (CMA) sampling.
Audit Training -

4)
 (d) Haphazard selection, in which the auditor selects the sample without
following a structured technique. Although no structured technique is used, the
auditor would nonetheless avoid any conscious bias or predictability (for example,
avoiding difficult to locate items, or always choosing or avoiding the first or last
entries on a page) and thus attempt to ensure that all items in the population have a
chance of selection. Haphazard selection is not appropriate when using statistical
sampling.
Audit Training -

4)
 (e) Block selection involves selection of a block(s) of contiguous items
from within the population. Block selection cannot ordinarily be used in audit
sampling because most populations are structured such that items in a sequence can
be expected to have similar characteristics to each other, but different characteristics
from items elsewhere in the population. Although in some circumstances it may be
an appropriate audit procedure to examine a block of items, it would rarely be an
appropriate sample selection technique when the auditor intends to draw valid
inferences about the entire population based on the sample.
Audit Training -

DETERMINING A SAMPLE SIZE
Using the ICAA Audit Manual

Using a confidence factor
Sampling can be completed by using a
confidence factor to help set the sample size.
Confidence factors are described in the ICAA
Australian Audit Manual (p.497)
Audit Training -

Determine a selection interval
A selection interval (j) is determined using the
following formula:

𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙𝑖𝑡𝑦
𝑐𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 𝑓𝑎𝑐𝑡𝑜𝑟
= 𝑗
Also expressed as:

𝑚𝑝
𝑟
= 𝑗
Audit Training -

Determine a sample size
A sample size is determined using the
following formula:

𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛
𝑗
= 𝑠𝑎𝑚𝑝𝑙𝑒 𝑠𝑖𝑧𝑒
An example:
Population = $177,203
Materiality = $15,000
Confidence factor = 3
Sample interval = $15,000/3 = $5,000
Sample size = $177,203/$5,000 = 35.4 (roundup to 35)
Audit Training -

 Using CaseWare’s template:
Audit Training -

Audit Training -

How do I select a confidence factor?
“professional
judgement.
Audit Training -

Presenter details
Source: LinkedIn
Matthew has been running
computer assisted audit
techniques to perform data
analysis since before the turn of
the century and has instructed
auditors in the use of IDEA, ACL
and Excel.

Presenter details
Source: LinkedIn

Thank you
www.hanrickcurran.com.au
Hanrick Curran
t. (07) 3218 3900
f. (07) 3218 3901
e. enquiries@hanrickcurran.com.au
Level 11
307 Queen Street
Brisbane Qld 4000
GPO Box 2268
Brisbane Qld 4001

Data Analysis for Audit Training (2016.06)

Recommended

Recommended

More Related Content

What's hot

What's hot (19)

Viewers also liked

Viewers also liked (9)

Similar to Data Analysis for Audit Training (2016.06)

Similar to Data Analysis for Audit Training (2016.06) (20)

More from Matthew Green

More from Matthew Green (11)

Recently uploaded

Recently uploaded (20)

Data Analysis for Audit Training (2016.06)