This document provides an overview of data analytics tools for internal auditors, highlighting various software like Excel, ACL, IDEA, and SAS. It details analytical techniques, data acquisition methods, and the significance of statistical analyses in auditing. Additionally, it introduces auditnet® as a resource for auditors, providing templates, training, and community networks.

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Data Analytics - 5
Data Analytics Software
based on Data Analytics for
Internal Auditors
by Richard Cascarino
About Jim Kaplan, CIA, CFE
 President and Founder of AuditNet®,
the global resource for auditors (now
available on iOS, Android and
Windows devices)
 Auditor, Web Site Guru,
 Internet for Auditors Pioneer
 Recipient of the IIA’s 2007 Bradford
Cadmus Memorial Award.
 Author of “The Auditor’s Guide to
Internet Resources” 2nd Edition
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About AuditNet® LLC
• AuditNet®, the global resource for auditors, is available on the
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Introductions
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The views expressed by the presenters do not necessarily represent
the views, positions, or opinions of AuditNet® LLC. These materials,
and the oral presentation accompanying them, are for educational
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While AuditNet® makes every effort to ensure information is
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Any mention of commercial products is for information only; it does
not imply recommendation or endorsement by AuditNet® LLC
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About Richard Cascarino, MBA,
CIA, CISM, CFE, CRMA
• Principal of Richard Cascarino &
Associates based in Colorado USA
• Over 28 years experience in IT audit
training and consultancy
• Past President of the Institute of
Internal Auditors in South Africa
• Member of ISACA
• Member of Association of Certified
Fraud Examiners
• Author of Data Analytics for Internal
Auditors
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Today’s Agenda
 Excel and Data Analysis
 ACL and Data Analysis
 IDEA and Data Analysis
 SAS and Data Analysis
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Excel and Data Analysis
Management Use of Excel
Use of Excel in financial analysis
Data Acquisition
Excel Database Functions
Excel Financial Functions
Ratio Analysis
Du Pont Analysis
Z-Score Analysis
ACL Add-in
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Management Use of Excel
 Risk management models
 Budgeting and forecasting
 Cash-flow models
 Costing models
 Inventory management
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Excel in Financial Analysis
 Requires:
Clear identification of the intention of the
analysis. This will commonly involve interviews
with the interested parties
Identification of the information the auditor are
already has available or has access to
Sanitizing the data to remove known erroneous
information which could distort the analysis
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Data Acquisition from a
Website
 the Get External Data from Web command
on the Data tab
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Data Acquisition from
Elsewhere
 Querying an external database
Get External Data on the Data tab
Microsoft query under Other Sources
Select ODBC driver
Microsoft Query Wizard.
 Importing a complete database table
Get External Data on the Data tab
Access, SQL Server, XML, or OLEDB
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Excel Database Functions
 Assist in the analysis of a large number of
organized data records
Formula tab
Insert a Function from the Database category
DSUM which calculates the sum of values in a field of a
list or database, which satisfy specified conditions
DSUM (database,field,criteria)
DAVERAGE which calculates the average of values in
a field of a list or database, which satisfy specified
conditions
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Excel Financial Functions
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Payment functions
Price functions
Investment value functions
Interest rate functions
Internal rate of return functions
Yield functions
Asset depreciation functions
Duration functions
Dollar functions
Ratios
 Liquidity ratios
 Coverage ratios
 Efficiency ratios
 Profitability ratios
 Leverage relations
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Du Pont Analysis
 A method of evaluating the return on equity
(ROE)
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ROE = = x
ROA = = x
= =
𝑅𝑂𝐸 = / 1 −
Du Pont Analysis
 ROE can be improved by:
Increasing the total asset turnover
Increasing the net profit margin
Increasing the amount of debt relative to
equity
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Z-Score Analysis
 Helps identify those organizations potentially at risk
to financial distress or bankruptcy
Z = 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + X5
 Where:
X1 = net working capital/total assets
X2 = retained earnings/total assets
X3 = EBIT/total assets
X4 = market value of all equity/book value of total liabilities
X5 = sales/total assets
 Altman concluded that an organization with a Z-score below 2.675
should have an expectation of severe financial distress within the
following year
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ACL Add-in
 Create read-only versions of information, formulas,
and cells to avoid errors
 Select samples for testing or investigation
 Summarize and filter, stratify, and age information
in Excel
 Multiple computed columns may be added to
 Notes may be added to multiple rows to indicate
that analyst’s intention as well as variables selected
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http://www.acl.com/products/acl-excel-add-in/
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ACL and Data Analysis
 Analyzing Live Data – dangers
Auditors may inadvertently corrupt live data
Audit tests may cause the live system to crash causing
processing disruption and corporate damage
Audit queries may significantly impact the speed of
processing in critical systems resulted in their client
dissatisfaction
 While the analysis is being conducted, the
data contents will move on
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Verifying the Data
 Generate statistics on the key fields
including:
Record counts
Calculation of totals and, when appropriate, key
subtotals
Average, minimum and maximum values where
appropriate
 Classify and Summarize commands within
the Analyze menu
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Basic Statistical Analysis
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Basic Statistical Analysis
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Importing Data into ACL
 Client-Server Capability
 Data with Electronic Definitions
ODBC
Delimited files with embedded field names
External electronic file definitions
 Data Definition Wizard
Selecting the data source
File analysis
Field analysis
Defining field properties
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Seeking Anomalous Data
 Violations of the organization’s policies and
procedures or legal violations of statue
Customers with account balances exceeding their credit
limits
Excessive use of sole vendors
Vendors with unusual or overseas bank accounts
Dormant vendors
Duplicate vendors
Duplicate employees
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Seeking Anomalous Data
Invalid Social Security numbers on employee records
Excessive use of overtime
Loans which are past due
Transactions over corporate limits
Multiple transactions to a single vendor in the same time
period at, or just below, transaction valued limits
 Auditor must predefine the extent of anomaly which
would be of audit significance
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Seeking Anomalous Data
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Joining and Merging Tables
 From two tables with differing structures, but
containing the common key field
 Common mistake this kind of join is choosing
the wrong file as the primary table
 Eg Joining Transactions with Suppliers
All transactions plus vendor name OR
All Vendors with one transaction each
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Joining and Merging Tables
 New table to contain:
Matched
Unmatched
Matched, all primary, all secondary
Matched, all primary
Matched, all secondary
Many-to-many
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Analysis Options
 Classify command
Quick data scan and summarize based on the
unique value of a character field while some
totaling specified numeric fields
 Histogram command
Produce 3-D vertical bar charts showing the
distribution of records based on the values of a
particular field of expression
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Analysis of Sales
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Analysis Options
 Age command
produces age summaries of the data facilitating
the valuation of trends in values and volumes as
well as verifying aging of outstanding balances
and the like
 Summarize command
Generate reports on any number of unique key
character and date values allowing numerical
fields to be totaled and counted for each key
value.
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Aging
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Analysis Options
 Cross-tabulate
facilitates the analysis of data fields in a tabular
form of rows and columns
 Perform Benford Analysis command
will generate a digital analysis using Benford’s
formula
also includes the Z-statistic which is used where
the sample size is large and the data is assumed
to come from a normal population of which the
variance is known
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Benford Analysis
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Analysis Options
 Look for Gaps / look for Duplicates
Commonly used by auditors to identify errors in
sequenced databases
 Sampling techniques
Monetary Unit Sampling
Record Sampling
Calculate the minimum sample size required to meet
the auditor-specified criteria as well as to evaluate the
impact of any errors detected within the sample
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Seeking Duplicates
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Sizing a Sample
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Use of Pivot Tables
 Cross tabulate selection within the Cross tabulate creation
within the Analyze menu
Here the Rows will be selected for PRODCLS, the Columns by LOC
and the Subtotal fields will be MKTVAL and Value.
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ACL Scripts
 A series of commands intended to perform a
specific task, and multiple tasks
Save and repeat commands
Share best audit practices with others
Ensure consistency of audit approach and
inquiries
Create interactive audit inquiries that can be run
by non- auditors
 Scripts may be interactive
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ACL Scripts
 Scripts may be:
Created or edited in the ACL Script Editor
Created using the Script Recorder
Created from a table history
Created from log entries
Copied from another ACL project
Exported from the project to be later used in
another ACL project
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IDEA and Data Analysis
 http://ideasupport.caseware.com/public/down
loadidea/
 Used for:
Anomalous items
The erroneous calculations
Gaps and duplicates within data
Trends within data over a period of time
Erroneous cross matching of data among systems
Statistical sampling
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IDEA and Data Analysis
 Used for :
detective examination of files seeking unusual
patterns of data
verification of processing controls through re-
calculations of values
direct file interrogations and standard audit
analyses
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As with ACL
 Standard audit analyses such as:
trend analysis
time series analysis
data correlation
gap detection
aging of transactions
Benford’s Law analysis
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Data Import
 Import data from a variety of sources including:
AS 400 databases
dBase files
Microsoft Access
Microsoft Excel
ODBC compliant databases
Print report and Adobe PDF files
SAP/AIS
Text files
XML data
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IDEA Work Area
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Where
 1. The Menu Bar
 2. Operations Toolbar
 3. File Explorer Toolbar
 4. File Explorer Window
 5. Database Window
 6. Flyout Windows
 7. Status Bar
 8. Properties Window
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Data View
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Properties Window
 View or edit the properties of the active file
including the:
Data
History
Field Statistics
Control Total
Criteria
Results
Indices
Comments
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Field Statistics
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Analytical Techniques
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Analytical Techniques
 Statistical Sampling
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Duplicates
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Other Analyses
 Correlation
 Regression
 Trend Analysis
 Time Series Analysis
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IDEA Scripts
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SAS and Data Analysis
 One of the most commonly used Large Scale
Statistical Analysis Systems
 Designed to access databases, as well as flat, un-
formatted files
 Programming oriented
SAS programming language
 Can examine large volumes of data to uncover
hidden patterns, correlations and other insights
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System Modules
 Base SAS
provides the ability to access data from multiple sources
in a variety of formats with the ability to handle data
manipulation, SQL and descriptive statistics
 SAS/STAT
provides routines for both univariate and multivariate
statistical modeling
 SAS/OR
allows the building of mathematical optimization models
for Operational Research purposes utilizing both linear
and nonlinear programming techniques. This makes it
ideal for network analysis in the audit frame
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System Modules
 SAS/ETS is the econometric and time series
module facilitating model building and time series
analysis
 SAS/Access contains a series of interfaces for
different vendor databases
 Other modules include SAS/AF, SAS/ Graph, SAS
QC, SAS/Share and SAS/Connect
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System Modules
 Free university Edition:
 http://www.sas.com/en_us/software/university-
edition.html
 It operates in a virtual environment such as
Oracle’s VM VirtualBox Manager also so freely
downloadable
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University Edition
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SAS Usage
 Fraud Detection
Anomaly detection – where alerts and triggered based on statistical
deviations from the norm
Business rules – where alerts are based on management’s general
experience and the application of the appropriate business rules
Predictive models – use alert triggers which established based on
scores derived from event characteristics that have seen to be
indicators of prior fraud events
Analysis of social networks – a more integrated method in which
through alerts are triggered based on known individuals suspected of
fraudulent behavior and associated with a current event or transaction
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SAS Enterprise Case
Management
 Can integrate data from multiple systems,
standardize data forms and apply and quality data
rules consistently across the organization
 Designed for forensic investigation and operation
mismanagement
 Can track cases across functions and business
units
 Facilitates recommending streamlining of business
processes to improve bottom line profitability
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Questions?
Any Questions?
Don’t be Shy!
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