1. Sean Walker, Managing Principal Ryan Merryman, Senior Manager
Using Data Analytics as a Management Tool to Identify Organizational Risks
Sean Walker, Managing Principal
Ryan Merryman, Senior Manager

2. Objectives
Discuss how data analytics can be used to better identify various risks in an organization.
Encourage you to use technology to protect the organization.
Demonstrate the power of data analytics using a case study.

3. What and Why Data Analytics?
Data analytics is the process of accessing, normalizing, and modeling data with the intent of discovering useful information
Often consider a forensic tool
Much more can be learned about your organization
Large organizations such as States process very large amount of data and often in a decentralized manner
Risk of misappropriations
Risk of management override of internal controls
Risk of the unknown

4. Misconceptions It’s hard. It’s time consuming. It’s expensive.
It’s an audit tool.
Get it done –
Outsource it
Get trained and get coached

5. Organizational Studies
What is normal for my organization?
What is abnormal?
Who is working in the system?
When are they working in the system?
Who is making all those journal entries?
What systems feed information to the financial system?

6. Types of Risks and Areas of Analysis
Accounts Payable
Fictitious vendors
Fictitious, inflated and / or duplicate invoices
Structured payments
Conflicts of interest
Kickbacks
Bid-rigging
Purchase Cards
Duplicate purchasing and reimbursement schemes
Unauthorized and/or improper purchases
Unauthorized users
Unauthorized SIC codes
Payroll
Ghost employees
Improper supplemental payments
Improper bonus or incentive compensation payments
Inflated salaries
Inflated hours
Travel and Entertainment Expense
False or inflated reimbursement submissions
Improper use of corporate credit card
Purchase for personal use
Foreign Corrupt Practices Act
Journal Entries
Unbalanced journal entries
Improper management override
Improper expense capitalization
Improper revenue recognition
Entries to unusual or seldom used accounts
Improper or unauthorized user activity
Entries during non-business hours

7. Types of Risks and Areas of Analysis
Accounts Receivable
Fictitious customers
Lapping
Credit balance fraud
Offsets with unauthorized or improper expenses
Improper AR aging
Inventory
Fictitious, inflated, duplicate or unnecessary purchases
Theft through improper write-off
Excessive shrinkage
Revenue
False or inflated sales
Fictitious customers
Improper commission or bonus payments
Revenue recognition abuses including channel stuffing, liberal return policies or bill and hold schemes
Non-Financial
Weblog analysis
Building access logs
Computer print reports
Client proprietary database analysis

8. State Specific Risks Internal control overrides General disbursements
Avoid purchase authority escalation
Journal entries
General disbursements
Payroll
Derived revenues (sales tax, etc.)
Beneficiary payments
Health and Human Service
Grant payments

9. State Pension Plan
Brief Case Study

10. Cash Disbursement Testing: Objective - Test 3
Cash Disbursement Testing: Objective - Test 3.1 Million Payments Totaling $7.0 Billion
Challenges faced by us: Each monthly report provided by the State:
Was in a massive .pdf “print report” format, each monthly files was approximately 15,000 pages
There were 3.1 Million payments in FY 2013
The files were too large to print and so large they crippled laptops
123 ft
360,000 pages
6 ft

11. Cash Disbursement Testing: The Monthly Report was Over 15,000 Pages

12. Most Systems Provide Data in a Usable Form
Time and Effort
Most Systems Provide Data in a Usable Form

13. Cash Disbursement Testing: Understanding the Data and Year over Year Comparisons
We were able to analyze the files and compare activity from FY 2013 to FY 2012

15. Benford’s Analysis
FY 2013
FY 2012

16. Stratification by Payment Amount
FY 2013
FY 2012

17. Cash Disbursement Testing: Abnormal Payments
Specific Analysis that would be difficult/impossible without Forensic Data Analysis:
Payees whose payments amounts varied significantly

18. Cash Disbursement Testing: Specific Analytics
Specific Analysis that would be difficult/impossible without Forensic Data Analysis:
Retirement Numbers that had more than one name associated

19. Cash Disbursement –Data Analysis vs. Traditional Procedures
Imported 100% of data
Reconciled totals to F/S for Completeness Testing
Summary Results that tie to F/S balances and compared to PY
Run specific queries from which to make Risk Based selections for test work
More efficient - Analysis FY 2013 procedure took only about 60% of the time of FY 2012
Traditional Procedure
Random Sampling
No Completeness Testing
Less efficient

20. Consider How much data is collected in your organization?
How quickly can you analyze the data for management decisions and internal risks?
As Comptroller’s do you believe you have your hands around all the state’s transactions?

21. Sean M. Walker, CPA, CGFM, CGMS Managing Principal State and Local Government Ryan Merryman, CPA/CFF/CITP, CFE Senior Manager Forensic Services