These slide accompany a video training presentation from AuditNet®. The video is available to view at http://bit.ly/1eBRLiZ (registration with AuditNet.tv required) Learning Objectives: Gain an appreciation, based on the attendee participants, of their successes and pitfalls when planning data analytics. Understand some common approaches to overcoming obstacles to planning data analytics based on case studies from companies and survey attendees themselves. Learn how planning analytics can be integrated into top audit areas. Outline an effective data request process to ensure complete and accurate extractions of data every time. See how analytics can maximize the annual audit plan and better ensure focus is placed on organizational risk.

1. Planning Data Analytics
Into Your Audits – Best
Practices
October 31, 2012
AuditNet and AuditSoftware.Net
Collaboration
Brought to you by AuditSoftware.net and
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Today focused on providing practical data
analysis training
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2. About Jim Kaplan, CIA, CFE
 President and Founder
of AuditNet®, the global
resource for auditors
 Auditor, Author, Web
Site Guru, Internet for
Auditors Pioneer
 Recipient of the IIA’s
2007 Bradford Cadmus
Memorial Award.
 Local Government
Auditors Lifetime
Member Award
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Introductions
About AuditNet LLC
• AuditNet® is the global resource for auditors created by Jim
Kaplan an Internet for auditors pioneer and recipient of the IIA’s
2007 Bradford Cadmus Memorial Award. The Web site features:
• Over 2,000 Reusable Templates, Audit Programs, Questionnaires,
and Control Matrices
• Training without Travel Webinars focusing on fraud, audit software
(ACL, IDEA, Excel), IT audit, and internal audit
• Audit guides, manuals, and books on audit basics and using audit
technology
• LinkedIn Networking Groups
• Monthly Newsletters with Expert Guest Columnists
• Book Reviews
• Surveys on timely topics for internal auditors
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3. Webinar Housekeeping
This webinar and its material are the property of AuditNet® and Cash
Recovery Partners. Unauthorized usage or recording of this webinar or
any of its material is strictly forbidden. We will be recording the webinar
and if you paid the registration fee you will be provided access to that
recording within two business days after the webinar. Downloading or
otherwise duplicating the webinar recording is expressly prohibited.
Please complete the evaluation to help us continuously improve our
Webinars
You must answer the polling questions to qualify for CPE per NASBA
Submit questions via the chat box on your screen and we will answer
them either during or at the conclusion
If GTW stops working you may need to close and restart. You can
always dial in and listen and follow along with the handout
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Richard B. Lanza, CPA, CFE, CGMA
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Over two decades of ACL and Excel software usage
Wrote the first practical ACL publication on how to use the
product in 101 ways (101 ACL Applications)
Has written and spoken on the use of audit data analytics for
over 15 years.
Received the Outstanding Achievement in Business Award by
the Association of Certified Fraud Examiners for developing
the publication Proactively Detecting Fraud Using Computer
Audit Reports as a research project for the IIA
Recently was a contributing author of:
•
•
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Global Technology Audit Guide (GTAG #13) Fraud in an
Automated World – Institute of Internal Auditors.
Data Analytics – A Practical Approach - research whitepaper
for the Information System Accountability Control
Association.
Cost Recovery – Turning Your Accounts Payable Department
into a Profit Center – Wiley and Sons.
Please see full bio at www.richlanza.com

4. Learning Objectives
Gain an appreciation, based on the attendee participants,
of their successes and pitfalls when planning data
analytics.
Understand some common approaches to overcoming
obstacles to planning data analytics based on case
studies from companies and survey attendees
themselves.
Learn how planning analytics can be integrated into top
audit areas.
Outline an effective data request process to ensure
complete and accurate extractions of data every time.
See how analytics can maximize the annual audit plan
and better ensure focus is placed on organizational risk.
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Technology Standards
 IPPF Standard 1210.A3
 Internal auditors must have
sufficient knowledge
of…available technology
based audit techniques to
perform their assigned
work

5. IIA Guidance – GTAG 13
Internal auditors require appropriate
skills and should use available
technological tools to help them
maintain a successful fraud
management program that covers
prevention, detection, and
investigation. As such, all audit
professionals — not just IT audit
specialists — are expected to be
increasingly proficient in areas such as
data analysis and the use of
technology to help them meet the
demands of the job.
Professional Guidance

6. Categories of Audit Software
Continuous
Anti
Controls
Fraud
Monitoring
Automated
Issues
Tracking
Governance
Risk
Compliance
Data
Analysis
Risk
Assessment
Audit
Management
Electronic
Work Papers
Audit
Resource
Scheduling
2012 Survey: Using Data
Analysis Software
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Over 500 auditors responded as of 11/01/2012
More than 70% reported using data analysis software
85% of those using reported purchasing specifically for data
analysis
68% reported use to improve audit plan sometimes or always
33% Ad Hoc Beginner (Excel) 37% Intermediate (Excel, ACL,
IDEA)
73% use audit staff for data analysis (no outsourcing)
44% use ACL, 33% use Access , 25% IDEA
43% major reason for not using on all audits - staff not trained
75% said greatest benefit - able to review entire population
84% performance objectives/compensation not tied to use
59% indicated would use data analytics if audit programs
included steps
58% indicated would use if a script library were available or if
vendors provided a lite version of their software
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7. Planning Data Analytics
Statistics From the Audience
AuditNet – 2012 Data Analysis Software
Survey – Why Are You Not Using D.A.?
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8. Today’s Attendees
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Today’s Attendees
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9. Today’s Attendees
Data Analytics in the Audit Plan
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Today’s Attendees
Data Analytics in Audit Planning
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10. Today’s Attendees
Data Analytics for Process Flow
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Polling Question #1
What is the top reason why data analytics is
not used in the audit?
 Upper management support
 Getting the data
 Planning it in to the audit
 I don’t know
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11. Common Approaches to Overcoming
Obstacles in Planning With Data Analytics
Planning Data Analytics
Identify the Risk Areas for the Audit
Objective
Risks Identified Best use of Data Analytics

12. Planning Data Analytics
Id the Risk Areas - Type of Analysis
Low: Volume / Complexity – Manual Analysis
Medium - High: Volume / Complexity –
Data Analytics Tools
Overcoming Obstacles
Use data analytics on almost every audit
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Brainstorm the use data analytics in the audit planning process
Risk assess the general ledger – stratify by month by account
Drop an audit and instead plan 10% for “data fun” across all audits
Make it part of “annual objectives”
Use low-cost solutions to start
 Excel is a great starter tool for small audit shops
 Add-ins to Excel can be your next stepping stone and all have 30-day trial
licenses
 Training can be self study, vendor videos, and webinar based
 Work your way up to the more advanced tools from a cost and training
perspective
Find cost savings to pay for the usage & Track it
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13. Integrating Analytics into Top Audit Areas
AuditNet – State of Technology Use Where Are Data Analytics Used?
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14. Audit Objectives
1. Purchasing and accounts payable activities
are operating effectively and efficiently
2. Expenses are properly authorized, accurate,
and complete
3. Receipts are accurate and complete
4. Check processing is safeguarded,
authorized, accurate, and complete
5. Audit trails are maintained and timely
information is provided to decision makers.
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Audit Objectives to Scripts
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15. How the Scripts Align
to Objectives
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Specific Tests Based
on the 5 Ws
Who
 Summarize journal entries by the persons entering to determine if they’re
authorized.
What
 Summarize journal entries by account and repetitive extracts (more than 50
instances) and unique account sequences used in the journal entry (based on the
first five debit and credit postings).
 Extract nonstandard or manual journal entries (versus a created system such as an
accounts payable ledger posting) for further analysis.
 Stratify size of journal entries based on amount (using the debit side of the
transaction).
 Summarize general ledger activity on the amount field (absolute value of debit or
credit) to identify the top occurring amounts. Then summarize activity by account
and the amount identified for the top 25 appearing amounts.
 Scatter-graph general ledger account (debit and credit amounts separately) and
numbers of transactions.
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16. Specific Tests Based
on the 5 Ws
When
 Extract journal entries posted on weekends and holidays.
 Extract journal entries relating to the prior year that were made just immediately
following a fiscal-year end.
 Summarize journal entry credits and debits processing by day, month, and year.
Where
 Extract journal entries made to suspense accounts and summarize by the person
entering and corresponding account numbers.
 Extract journal entries to general ledger accounts known to be problems or complex
based on past issues (errors of accounting in journal subsequently corrected by
accounting staff or auditors) at the company or the industry in general.
 Extract debits in revenue and summarize by general ledger account. Summarize
journal entries by the persons entering to determine if they’re authorized.
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Specific Tests Based
on the 5 Ws
Why
 Extract general ledger transaction amounts (debit or credit) that exceed the average
amounts for that general ledger account by a specified percentage. (Five times the
average is the default.)
 Extract journal entries that equate to round multiples of 10,000, 100,000, and
1,000,000.
 Extract journal entries with key texts such as “plug” and “net to zero” anywhere in
the record.
 Extract journal entries that are made below set accounting department approval
limits especially multiple entries of amounts below such limits.
 Extract journal entries that don’t net to zero (debits less credits).
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17. Mapping Data Elements to
Audit Objectives
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Polling Question #2
Which audit objective question below is most
easily automated?
 Does the company have a written code of ethics?
 Does the company follow approval limits prior to
invoice approval?
 Do adequate written procedures exist for invoice
processing?
 Is check stock safeguarded?
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18. Effective Data Import Process
Top 10 Data Import Mistakes
1. Not knowing what is possible within the tool to
import and normalize data
2. Asking for data before understanding reporting
needs
3. Not including knowledgeable system
professionals to assist in or review the extract
4. Forgetting to run statistics on amount/date fields
5. Not summarizing text code fields (including
invoice numbers to find E+ issues)
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19. Top 10 Data Import Mistakes
6. Lack of hardcopy information for review in
relation to imported data
7. Not validating field totals to batch totals
8. Using report files vs. fixed length system files
9. Getting data in Excel vs. a more raw format
10.Lack of understanding of the various data
types
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Quick Process to Running
Data
1.
2.
3.
4.
5.
6.
7.
Know your audit objectives
Align reports to the objectives
Use past reports to model /refine reports
Set data requirements based on reports
Obtain, validate, and normalize data
Edit scripts for data needs
Run reports and document results
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20. Data Request Checklist
Actual Files to Obtain
 File Structure / Record Layout
 Indexes to Understand Data
 Indexes to Understand Reason Codes
Other non-System Information Needed
 Loan or Credit Agreement Terms
Data Request Checklist
Computed Fields
• How to use them?
• Where to place them?

21. Data Request Checklist
Repetitive Audit /
Project
vs.
Special Assignments
Data Request Checklist
Outcome
Initial Analysis
Next Steps

22. Today’s Attendees
Data Request is Sent Prior…..
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Clear Data Request
Accounts Payable Data Request.doc
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23. Polling Question #3
What is NOT one of the top 10 data import
mistakes?
 Asking for data before understanding report
needs
 Not validating batch totals to data
 Including knowledgeable people in the extract
process
 Not knowing what is possible in the software
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Integrating Analytics into The Audit Plan

24. Cost Recovery Opportunity Analysis
Expenses for Analysis
 Primarily SG&A
 Cost of goods sold (i.e., freight)
Data Files
 General Ledger (trial balance)
 A/P Invoice Detail Distribution
 Purchase Orders
 Pricing List
Profit Opportunities
Outweigh Analytic Costs
Accounts Payable
Audit Fee Benchmarking
Advertising Agency
Document Fleet
Freight
Health Benefits
Lease
Media
Order to Cash
Proactive Fraud
Detection
Project Fraud
Real Estate Depreciation
Sales & Use Tax / VAT /
R&D tax
Strategic Sourcing
Telecom
Travel and Entertainment
Utilities

25. Cost Recovery Opportunity Tests
A/P and G/L Review Factors
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Accounts that are sole sourced
Accounts that have too many vendors
Categories that map to the “recovery list”
Assess to industry cost category benchmarks
Top 100 vendors
Trend analysis over time
Trend analysis by vendor (scatter graph)
Purchase Order / Price List
 Match to invoice payments to assess price
differences
 Strategic sourcing vendor review
Stratify Your Data
=IF(B4>1000,“3. Over $1000",IF(B4>100,“2. Over
$100 to $1,000",IF(B4<=100,“1. Up to $100")))
This will create three strata:
1. Up to $100
2. Over $100 to $1,000
3. Over $1,000
Start from highest to lowest – Excel picks the first
matching item
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26. The Sampling “Problem”
Bottom Line Numbers
Modern tests (round numbers, duplicates, missing fields)
identify thousands of ‘suspicious’ transactions, usually about 1
in 5 of all transactions get a ‘red flag’
Historically at least 0.02 – 0.03 % of all transactions have real
problems, such as a recoverable over-payment
So roughly 0.00025 / 0.2 = 0.00125 or 1 in 800 ‘red flags’ lead
to a real problem.
Imagine throwing a random dart at 800 balloons hoping
to hit the right one!!!
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Transactional Score
A single score is given to each transaction based on its severity
(number of attributes it meets)
Scores are summarized by enterer, vendor, and department (buyer)
Scattergraphs are completed of the results by:
 Enterer
 Business Partner
 Department
…focusing on severity/volume and differences in
these variables
Sampling is completed in each quadrant
51

27. Transactional Scoring
The result is a sampling
methodology that is now
based on Risk as you define
Page 52
Summaries on Various
Perspectives
Summarize by
dimensions (and sub
dimension) to pinpoint
within the cube the
crossover between the top
scored location, time, and
place of fraud based on
the combined judgmental
and statistical score
53

28. Using Vlookup to Combine
Scores
Create a record number
Relate sheets based on VLookup
Page 54
Severity To Value
55

29. GeoMapping – BatchGeo
Page 56
Polling Question #4
What function is mainly used to align all
scores in a spreadsheet?
 MOD()
 FIND()
 MID()
 VLOOKUP()
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30. Questions?
Any Questions?
Don’t be Shy!
Page 58
AuditSoftwareVideos.com
Videos accessible for 12-month subscriptions
Repeat video and text instruction as much as
you need
Bite-size video format (3 to 10 minutes)
Professionally produced
videos
 Sample files, scripts,
and macros included for
ACL™ and Excel™
 Instructors with over 20
years experience in
ACL™, Excel™ , and
more
Page 59

31. AuditNet® Survey - 2012 Data
Analysis Software Survey
Please help us by taking the survey
Scan the QR Code with your Mobile Device
Or Visit
https://www.surveymonkey.com/s/2012DataA
nalysisSoftware
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Thank You!
Jim Kaplan
AuditNet LLC®
1-800-385-1625
Email: webinars@auditnet.org
http://www.auditnet.org
Richard B. Lanza, CPA, CFE
Cash Recovery Partners, LLC
Phone: 973-729-3944
Cell: 201-650-4150
Fax: 973-270-2428
Email: rich@richlanza.com
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