Data analysis for auditors presented at CA ANZ 2018 Audit Conference

www.hanrickcurran.com.au
Data Analysis for Auditors
November 2017

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

McNamara – The Fog of War
1. Empathize with your enemy
2. Rationality will not save us
3. There's something beyond one's self
4. Maximize efficiency
5. Proportionality should be a guideline in war
6. Get the data
7. Belief and seeing are often both wrong
8. Be prepared to re-examine your reasoning
9. In order to do good, you may have to engage in
evil
10.Never say never
11.You can't change human nature
Eleven Lessons from the Life of Robert S. McNamara
These topics were selected by Errol Morris and highlighted in the 2003 documentary
film; they were not selected by McNamara.
Data analysis provides a window
into the data, enabling decisions
and analysis

Data isn’t information
A data set, such as a transaction list, does not
provide easily understood information. This is
why data analysis tools are particularly useful.
Source: Hanrick Curran data analysis training file

Today’s agenda …
1. Benford’s test
2. Efficiency benefits from using data analysis (using the
CaseWare sampling workpaper)
3. The basics for all data analysis work
4. Basic data analysis in Excel™ using pivot tables and
vlookup
5. Demonstrate basic use of TeamMate™
6. Using stratification for population analysis
7. The importance of the graphical display of information
8. Software options
9. Examine hurdles to adoption

7experience. new thinkingexperience. new thinking 7
This isn’t new for auditors, this is what we do.
A brief history of data analysis
by auditors

Auditors and data analysis
“Auditors have been
doing ‘data analysis’
since before that term
was cool.
We just called it
‘Computer Assisted
Audit Techniques’
(“CAAT”).

Data mining and big data
 Google data mining and you get 97,500,000 results
 Google big data and you get 774,000,000 results
 The explosion of data and its use is not going away
 Auditors need to respond to the massive increase in data that
clients can make available

A history in data analysis
Proof of data analysis
work in our profession
dates back to at least
the 1979 book about
CAATs, issued by the
AICPA.
AICPA – American Institute of Certified
Public Accountants

In 1999 …
“We were moving
300MB transactional
data files using reel-to-
reel computer tapes
and our IT team had to
get involved in moving
the files around.

In 1999 …
 Excel 97 had a capacity of 65,000 rows, so
Excel wasn’t the choice for most of our data
analysis.

A statistical test for identifying potential
fraudulent and anomalous transactions in large
data sets.
Trivia question …
Name the 2016 movie where Benford’s Law was
a central element of the plot.
Benford’s Test

The history of Benford’s Law
The story of Benford's Law begins in 1881, when astronomer Simon Newcomb noticed that the page
numbers in a book of logarithm tables were worn (or smeared) more toward the front of the book and
progressively less worn toward the end of the book. Where others would simply dismiss the worn page
numbers, Newcomb recognized a distinct pattern related to the occurrence of lower versus higher
numbers. He published an article explaining his observations and postulated that the probability of a
single number n being the first digit of a number was equal to log(n+1) − log(n). Fifty-seven years
later, in 1938, physicist Frank Benford tested Newcomb's hypothesis against 20 sets of data and
published a scholarly paper verifying the law. Despite Newcomb's groundwork, Benford has garnered
much of the credit for the discovery now commonly referred to as Benford's Law.
The application of Benford's Law to spot signs of accounting fraud grew out of an article published in
1972 by economist Hal Varian, who wrote that Benford's Law might be used to detect the possibility of
fraud in socioeconomic data submitted in support of various public planning decisions. Varian's general
idea was that a simple comparison of first-digit frequency distributions ought to reveal anomalous
results (if any), per Benford's Law. In 1999, a JofA article ("I've Got Your Number," May 1999), written
by Mark J. Nigrini introduced how forensic accountants and auditors could apply Benford's Law to search
for indicators of potential accounting and expenses fraud.
Benford attempted to explain his law by saying that "it's easier to own one acre than nine acres,"
implying (perhaps) that when people purchase land, it is reasonable to assume that more people
purchase one acre as a starting point, rather than nine acres as their starting point.
Source: https://www.journalofaccountancy.com/issues/2017/apr/excel-and-benfords-law-to-detect-fraud.html

Benford's Test
Source: Wikipedia

Benford's Test
 Benford’s test can
be run on all data
sets to identify
anomalous
transactions that
may warrant
further
investigation
Source: HC Audit File – expenses testing

Investigating anomalies
 We investigate anomalies, using the extraction ability inside TeamMate/Pivot tables.
 Anomalous populations are extracted and analysed for further consideration.
Source: HC Audit File – expenses testing

Benford’s Test
 Determined to
investigate the
transactions occurring
with an 8 or 9 as a
starting value.

Benford’s Test
Emailed client a selection of the data, with a
request to review and confirm whether further
investigation is required.

Benford’s followup
We start with a request to
management and follow
through on a response for our
audit files.

Benford's Test
Arises from
the ex-GST
price for a
$100 sale

Source: HC Audit File

Trivia question …
Name the 2016 movie where Benford’s Law was
a central element of the plot.
Movie question

Benford’s resources
 https://www.isaca.org/Journal/archives/2010/Volu
me-1/Pages/Using-Spreadsheets-and-Benford-s-
Law-to-Test-Accounting-Data1.aspx
 https://www.isaca.org/Journal/archives/2011/Volu
me-3/Pages/Understanding-and-Applying-Benfords-
Law.aspx
 https://www.journalofaccountancy.com/issues/2017
/apr/excel-and-benfords-law-to-detect-fraud.html

Understanding the requirements of ASA 500 and
how Data Analysis can help produce a more
efficient and effective audit. This is to help you
understand why data analysis is worth including
in your audit planning.
Audit Evidence

ISA 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.

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

 When testing specific items, the question
that arises is “what evidence do you
have for the remainder of the
population?”
 Data analysis can help you answer that
question.

Understanding the requirements of ISA 530
Audit sampling

Using the CA ANZ Audit Manual
Determining a sample size

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 CA
Australian Audit Manual (p.497)

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

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 36)

Determine a sample size
Using CaseWare’s template:

Audit benefits

Impact of “Other Substantive Tests”

Pro tip …
“ Other Substantive Tests may include your
data analysis procedures.

How do I select a confidence factor?
“Professional
judgement.

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

Auditor’s Toolbox
Source: Australian Audit Manual and Toolkit 2010, Using Clarity Standards (2010)

6P
 Prior
 Preparation and
 Planning
 Prevents
 Poor
 Performance

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 – a type of data table
 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.

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 (Outlook is great
for these)

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)

Pro tip …
“Junior staff will typically keep unnecessary
data, both in the audit file and in their own
systems (i.e., c:drive and email). Consider
your Firm’s systems and processes to get
them to clean out this data.

CaseWare “do not retain” button
 An easy way to manage
document retention is
using the “Retain on
Cleanup” checkbox in
CaseWare.
 Unchecking the “Retain
on Cleanup” box means
that the documents can
be automatically
excluded as part of the
file lockdown procedures.
Source: Hanrick Curran audit file

Basics in summary
 Plan ahead (6P)
 Track control totals
 Protect evidence
 Document as you go

Excel can be used for much of our basic data
analysis.
Excel™ analysis

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

Filters
Various number filters
offer a great starting
point for basic data
analysis in Excel™.

Pivot tables
Pivot tables provide
an even more
powerful way to
analyse data in
Excel™.
Create pivot tables
from the INSERT tab
on the Excel Ribbon.

Pivot tables
When creating the pivot table, ensure that all
of the cells in your data table are selected.
This is a good opportunity to check your
control totals.

A pivot table example
We can use a pivot table to summarise a transaction
register.
In this example, we can summarise the payment register to
obtain insight into the types of transactions recorded.

In this
example, we
can see the
types of
transactions
being
recorded in
the register,
by supplier.

This can be done by
dragging and dropping
fields from the list of
fields into the four
boxes at the bottom,
which then results in
automatically sorting
and summarising the
list we saw on the
previous slide.

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

Vlookup example
To cross check employee bank accounts
against supplier bank accounts, start with the
employee bank accounts.

Vlookup example
Using our payments register from earlier, let’s
look at how we would do a vlookup.

Vlookup example
With the payables register, we have to tweak
the layout to move the bank account to the
beginning of the data table.

Vlookup example
Excel provides great
support around the
function, with this function
wizard.
The data field you are
looking up to, must be the
first field (column) in the
target data set.
Don’t forget to use $A$1
references when linking to
the “table array” (i.e., your
data table).

Vlookup

Vlookup
This shows us that Mr Alfred ‘Ginger’
Cotton, from our payroll data file, has
the same bank account as supplier
MAR004 in our payables transaction
register.
A matter for further audit investigation.

TB editing
 TB’s come in many formats, often with the
“-” or credit sign at the back of numbers.

TB editing – instructions for back-to-front
minus signs
Steps to fix the TB
 Use TeamMate / ASAP-Utilities ? (see http://www.asap-utilities.com/)
or
 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

ABN lookup tool (for Aust.)

Screen clippings
(documenting your work)
 Use the screen clipping button from the
Quick Access Toolbar** to capture screen
shots for documenting audit testing
**

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

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.
 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.

Manipulate fields
 A tool inside TeamMate that can be used for
data manipulation
 Can be used for different analysis and is
worth exploring
 Highlights :
 Normalize fields – tool to standardize fields
 Debit and credit columns – splits/combines Dr and Cr columns
 Merge wrap and autofit – combines those functions from Excel into
one

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

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

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).

 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

If you want to explore more, visit video
demonstrations website:
http://www.teammatesolutions.com/data-analytics.aspx
Or search at: www.youtube.com

Fantastic tool used to understand the data set
and audit population. Best performed using
some type of audit / data analysis software.
Stratification is especially useful for identifying
significant and unusual items.
Stratification

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

Section audit approach
 Our standard
approach to auditing
a section includes
data analysis.
 Our team are
expected to run
stratification and
Benford’s tests on all
large data files (e.g.,
revenue and
expense transaction
files.
Leadsheet
Programs
Reconciliations
{PBC}
Analytical
review
Data analysis
Substantive
Testing
Self-review

Stratify
 Example of revenue stratification from small
client with significant related entities

Stratify
 Example of revenue
stratification from
large retail business

Stratification bands
 When setting stratification bands, consider
usual transaction amount for client.
 Also consider materiality thresholds, such
as: clearly trivial, performance materiality,
planning materiality, etc.

Stratification mistake

Pro tip …
“Stratification is the perfect tool for
identifying significant and unusual items.

Graphing outputs of spreadsheets is important.
Some examples of why follow…
A segue into the graphic display
of information

Anscombe’s Quartet
Four data sets with
similar characteristics
X average = 9.0
Y average = 7.5
X sum = 99.0
Y sum = 82.5
x y x y x y x y
10.00 8.04 10.00 9.14 10.00 7.46 8.00 6.58
8.00 6.95 8.00 8.14 8.00 6.77 8.00 5.76
13.00 7.58 13.00 8.74 13.00 12.74 8.00 7.71
9.00 8.81 9.00 8.77 9.00 7.11 8.00 8.84
11.00 8.33 11.00 9.26 11.00 7.81 8.00 8.47
14.00 9.96 14.00 8.10 14.00 8.84 8.00 7.04
6.00 7.24 6.00 6.13 6.00 6.08 8.00 5.25
4.00 4.26 4.00 3.10 4.00 5.39 19.00 12.50
12.00 10.84 12.00 9.13 12.00 8.15 8.00 5.56
7.00 4.82 7.00 7.26 7.00 6.42 8.00 7.91
5.00 5.68 5.00 4.74 5.00 5.73 8.00 6.89
sum
99.0 82.5 99.0 82.5 99.0 82.5 99.0 82.5
Average
9.0 7.5 9.0 7.5 9.0 7.5 9.0 7.5
I II III IV
Source: Wikipedia

Anscombe’s Quartet
-
2.00
4.00
6.00
8.00
10.00
12.00
- 5.00 10.00 15.00
Series I
-
2.00
4.00
6.00
8.00
10.00
- 5.00 10.00 15.00
Series II
-
5.00
10.00
15.00
- 5.00 10.00 15.00
Series III
-
5.00
10.00
15.00
- 5.00 10.00 15.00 20.00
Series IV

A common audit test
 A common audit test is to graph revenue, looking for spikes,
trends and seasonality.
 In these examples, two audit clients, displaying seasonality in
accordance with underlying business model.
 One factor we look for is a year-end spike.
$-
$100,000
$200,000
$300,000
July
August
September
October
November
December
January
February
March
April
May
June
Letting fees
$-
$50,000
$100,000
$150,000
$200,000
July
August
Septem…
October
November
December
January
February
March
April
May
June
Management Fees

Discontinuity
 Other common issues
include discontinuities
such as spikes, slope
changes and steps.
 Graphing outputs can
also help with
identifying spikes from
data entry or formula
errors.
Source: F1F9, 31 day on-line learning

Stephen Few, Perceptual Edge
 Stephen Few’s work on visual communication is well worth
investigating as part of developing your team’s use of excel.
 Typically a board paper might include a table such as exhibit A.
The problem with this is that the data does not provide the
reader with any insight into the data.
 Using Excel’s graphs, providing a visual presentation of the
graph allows insights (see next slide).
Sales ($'000) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Domestic 1,893 2,343 2,593 2,283 2,574 2,838 2,382 2,634 2,938 2,739 2,983 3,493
International 574 636 673 593 644 679 593 139 599 583 602 690
2,467 2,979 3,266 2,876 3,218 3,517 2,975 2,773 3,537 3,322 3,585 4,183
Exhibit A: Sales data table
Source: Stephen Few “Visual Communication” IBM Whitepaper, April 2009 (p. 2)

Stephen Few, Perceptual Edge
From the data at right for a typical sales graph, we can
observe:
 Domesitc sales trend upwards across the year
 International sales are relatively flat across the year
 An exception in international sales is noted in August
 There is a cyclical pattern in domestic sales, being
lowest in the first month of the quarter and then
growing through the quarter
From the graph, we might infer:
 Sales staff may be going light in the first month of
the quarter and start working harder as the quarter
progresses in order to meet their quarterly targets.
 Perhaps there is an element of ‘channel stuffing’
happening at the end of the quarter.
 Why the year-end spike?
Using Excel’s full potential enables this analysis.
-
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
Jan Mar May Jul Sep Nov
Domestic InternationalSource: Stephen Few “Visual Communication” IBM Whitepaper, April 2009 (p. 2)

Time to take a look at a more powerful tool, in
this case, IDEA.
An overview of IDEA

What is IDEA
 IDEA is a data analysis application (tool)
 IDEA can be run on a laptop (microcomputer) or be loaded onto a
clients network/servers/mainframe computers
(subject to licensing)
 IDEA is designed to analyse data sets (i.e., rows and columns of
data)

Where can you use IDEA
 If IDEA is loaded to a laptop, you can work from the clients
premises.
 The advantage of this is that as issues are identified, they can be
discussed with the client.
 More commonly, the analysis is performed as part of the audit
field work.

What hardware is needed for IDEA
 Generally any reasonably modern laptop should be able to handle
IDEA
 The better the machine, the better the analysis performance
 When I as a junior auditor, sometimes I would set up tests and
then let them run overnight . . . (but I think computer
performance has improved since then)

What does it cost $$$$$
 Not sure . . .
 In my experience, the biggest costs are:
training and time to use

Are there alternatives? YES
 Think about the limitations of excel . . .

SOME SLIDES FROM IDEA’S
DISTRIBUTOR FOLLOW

114
j
IDEA allows you to import
and export data from and
into a multitude of formats,
including files originating
from large mainframe
computers and accounting
software.

115
• IDEA protects the source data by allowing read-only access to the
client's data to avoid any unwanted changes, and maintain data
integrity.
• IDEA allows you to import and export data into a multitude of formats,
including files originating from large mainframe computers and
accounting software.
• IDEA allows you to easily manage your files and results and showing the
source of your results
• IDEA can read and process millions of records in seconds. There is no
limit to the number of records that IDEA can process.

116
IDEA creates a record of all changes made to a file
(database) and maintains an audit trail or log of all
operations carried out on a database, including the
import and each audit test.

117

118
 Testing entering data controls
 Data dictionary errors (undefined codes)
 System weaknesses (Code 99, Free style fields)

119
Audit Types:
 Quantitive analysis
 Testing efficiency and effectiveness (e.g.,
delays)
 Identify specific transactions and trends
(e.g., fraud)
 Testing controls (e.g., electronic signature)
 Compliance with regulation or policies
 Analytical reviews (e.g., salary)
 Matching information between different files

Fraud investigations
 Purchasing, payroll, money
laundering
Financial and other analyses
 Customer and other sales
profiling
 Performance measures
(Delays)
 Inventory – movement,
pricing
Security reviews
 Network, phone, firewall logs
Operations audits
 Policy compliance
 Value for Money
System audits
 Reliability of the data
 System weakness (code 99,
free style)

Fraud investigations
Payroll
 Ghost employees
 Changes to salary
 Changes to benefits
 Overtime payments
Purchasing
 Favorable treatment of vendors
 Transactions at or near spending authorities
 Large purchases over several invoices
 Phantom goods and phantom vendors
Money Laundering
 Applying Benford’s Law
 Funds from various accounts
 Accounts with a large average value of
transactions.
 Multiple accounts for particular individuals

Hurdles to employing data analysis in audit
assignments.
Hurdles and roadblocks

IMHO the biggest are:
People’s skill and experience in working
with data
Time required to complete analysis vs
audit budgets

Pro tip …
“ To overcome these problems, you need to
be prepared to take a risk, try something
different and make some mistakes. A culture
inside an audit firm that doesn’t allow people
to ‘fail’ won’t see a rapid, widespread
adoption of data analysis tools.

Other roadblocks
 Data access – client consent required and reluctance to give
access to systems
 Data access – requirements of privacy legislation
 Data access – format of data
(but has gotten better over the years)
 A lack of time to properly plan the procedures

Other hurdles
 Lots of data laying around = litigation risk
 Teams need good discipline in cleaning up client data files
 We suggest trying to limit “CYA” files kept by junior staff

Some of the benefits of using data analysis
include an improvement in audit effectiveness
and efficiency.
When to use data analysis

Data analysis enables testing
 Can test the entire population
 Limits sampling risk
 Still need to pay attention to accuracy and completeness of source
data
 One of the benefits of IDEA vs Excel is that IDEA doesn’t change the
source data
 IDEA also tracks the work performed with an audit trail
 But, Excel has a significantly lower entry cost than IDEA.

KEY areas to use data analysis
 Sampling (i.e., CMA / DUS / MUS )
 Stratification
 Extraction of unusual items
 Benford’s test

Client size for data analysis
 Audit fee < $10,000 = probably not worth it, unless you are
looking for something or want to perform a simple sample
selection
 Audit fee > $30,000 = starts to be useful for expenditure and
payroll testing for selecting samples for testing

Client industries for data analysis
 Generally good for industries with large transaction volumes
where transaction amounts are small dollar value
 Useful for financial services and retail clients
 Useful with services and manufacturing clients

Planning is key!
 When you are going to use data analysis, plan for it
 Discuss it with the client and get their buy-in
 Identify what tests will be performed with CAATs / data analysis
 Identify what data will be needed,
in what format,
at what date it is required, and
who will provide it

What do clients think?
 We don’t usually go through the testing approach with them, but
they love it when we have data that they don’t.
 It is especially useful to have the data to back-up management
letter comments (e.g., “56% of receivables population is outside
your determined credit limits”).

A summary of our “Pro tips”
Conclusion

Pro tip …
“ Other Substantive Tests may include your
data analysis procedures.

Pro tip …
“Junior staff will typically keep unnecessary
data, both in the audit file and in their own
systems (i.e., c:drive and email). Consider
your Firm’s systems and processes to get
them to clean out this data.

Pro tip …
“ With data analaysis:
Plan ahead
Track control totals
Protect evidence
Document as you go

Pro tip …
“ To overcome these problems, you need to
be prepared to take a risk, try something
different and make some mistakes. A culture
inside an audit firm that doesn’t allow people
to ‘fail’ won’t see a rapid, widespread
adoption of data analysis tools.

Pro tip …
“Stratification is the perfect tool for
identifying significant and unusual items.

Please let us know your questions or doubtful
points?
Questions

More resources
www.f1f9.com
“Well worth trying their 31 day free online course for a brush-up on your excel skills.”
www.spreadsheetdetective.com
“Use the tools we use, to understand and self-audit your model.”
www.asap-utilities.com
“Great tools for every excel user. If you ever work with data, you need these tools.”
www.perceptualedge.com
“For enlightening analysis and communication.”

Disclaimers
This document contains information in summary form and is therefore intended for general
guidance only. It is not intended to be a substitute for detailed research or the exercise of
professional judgement. It does not purport to be comprehensive or to render
professional advice. The reader should not act on the basis of any matter contained in this
publication without first obtaining specific professional advice.
We believe that the statements made by us in this document are accurate but no warranty
of accuracy or reliability is given. Our conclusions are based on interpretations of
accounting standards and other relevant professional pronouncements and legislation
current as at the date of this document. Should the interpretations, accounting standards,
other relevant professional pronouncements or legislation change, our conclusions may
not be valid. We are under no obligation to update the matters considered in this
document after its publication.
© Matthew J. Green FCA, March 2018
All rights reserved

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 prepared and
conducted instruction courses for
auditors in the use of IDEA, ACL
and Excel.

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

Summary slides for key requirements from
Australian Auditing Standards
Appendix – ASA requirements

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.

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.

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.

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.

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

ISA 530 Audit 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.

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.

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

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.

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.

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.

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

(ASA 530.App 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.

(ASA 530.App 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.

(ASA 530.App 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.

(ASA 530.App 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.

Data analysis for auditors presented at CA ANZ 2018 Audit Conference

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