Course Level: Intermediate
CPE Credits: 8.0 (1 day)
Looking for a "hands-on" course that leads you through the specific
commands, functions, and other features of ACL to identify potential fraud,
waste or errors in your data? Tired of attending generic data analysis
"theory" courses that do not provide you with actual examples of such
testing using ACL? Well look no further... this is the course for you!
This 1-day intensive, “hands-on” training course will provide you with the
know-how in using ACL to identify potential fraud, waste, and errors in your
data. Not just theory but actual step-by-step procedures that you can
readily apply within your organization.
At the conclusion of this course, the participant will be able to:
- Understand the role technology plays in the identification of fraud,
waste, and errors.
- Develop and execute ACL routines for testing the quality and
integrity of data.
- Understand the concept of data normalization and how to
normalize data to obtain consistent data formatting using ACL.
- Utilize specific ACL commands, command combinations, functions
and other features to effectively analyze data and identify potential
errors and indicators of fraud in the data.
- Utilize ACL's new duplicates detection algorithm and perform "fuzzy
logic" duplicates testing when necessary and appropriate.
- Effectively utilize Benford's Law to identify abnormal occurrences of
specific digits or digit combinations.
- Lecture / discussion: 50%
- In-class applications: 20%
- Section exercises: 30%
Participants should a good understanding of ACL basics, such as project
creation, command use, creation of computed fields, etc. It is strongly
recommended the participant have attended the "Introduction to ACL"
training (or similar) course.
Who Should Attend:
IS/IT Auditors, Fraud Examiners, Financial and Operational Auditors,
Internal/External Auditors, Analysts, Accounting Professionals,
Investigators, or anyone interested in learning how to use ACL to identify
potential fraud, waste, or errors in computer databases.
PIA Consulting will provide comprehensive course manuals containing
presentation material and class exercises.
A. Course Introduction and Overview
- Definition of fraud, waste, and error.
- Technology’s role in detecting fraud and error.
B. General Statistics Related to Fraud, Waste and Error
C. Verifying Data Quality and Integrity
- Data Quality and Integrity defined.
- Verifying data quality and integrity using ACL.
D. Data Normalization
- Data normalization (DN) defined.
- Why DN is critical to effective data analysis.
- Why it's a bad idea to do this in Excel or Access.
- ACL Functions & features useful for data normalization.
E. Step-by-Step Procedures
- Sorting on character and numeric fields to identify missing key data
or unusual transactions.
- Using ACL's Gap command to detect missing, cancelled, or
- Using ACL's improved (ver 9.3) Duplicates command algorithm to
create "fuzzy logic" and other powerful duplicates testing.
- Creating powerful ACL Search / Find routines to test for unusual
descriptions and/or data.
- Using field statistics to detect unusual account balances or
- Using ACL functions to detect weekend and after-hours transactions.
- Using numeric stratifications to identify possible circumvention of
approval authority levels.
- Other related tests.
F. Other Generic Testing Procedures
- Accounts Payable
- Accounts Receivable
- General Ledger
- Travel & Entertainment
- Vendor Management
- Procurement and Credit Card Transactions
G. Benford's Law / Digital Analysis
- Benford’s Law defined.
- Using the ACL Benford command.
- Understanding the results.
|PIA Consulting is registered with the National Association of State Boards |
of Accountancy (NASBA) as a sponsor of continuing professional
education on the National Registry of CPE Sponsors. State boards of
accountancy have final authority on the acceptance of individual courses
for CPE credit. Complaints regarding registered sponsors may be
addressed to the National Registry of CPE Sponsors, 150 Fourth Avenue
North, Nashville, TN, 37219-2417. Web site: www.nasba.org.
|Using ACL to Detect Fraud|