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©2013 CliftonLarsonAllen LLP cliftonlarsonallen.com See CLA PowerPoint User Guide for instructions to insert an image or change the icon on the business.

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Presentation on theme: "©2013 CliftonLarsonAllen LLP cliftonlarsonallen.com See CLA PowerPoint User Guide for instructions to insert an image or change the icon on the business."— Presentation transcript:

1 ©2013 CliftonLarsonAllen LLP cliftonlarsonallen.com See CLA PowerPoint User Guide for instructions to insert an image or change the icon on the business card. Find it at the bottom of the myCLA / Firm Resources / Materials / Templates page. See CLA PowerPoint User Guide for instructions to insert an image or change the icon on the business card. Find it at the bottom of the myCLA / Firm Resources / Materials / Templates page. Using Data Analytics as a Management Tool to Identify Organizational Risks Sean Walker, Managing Principal Ryan Merryman, Senior Manager

2 ©2013 CliftonLarsonAllen LLP 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. 2

3 ©2013 CliftonLarsonAllen LLP 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 3

4 ©2013 CliftonLarsonAllen LLP Misconceptions Its hard. Its time consuming. Its expensive. Its an audit tool. Get it done – –Outsource it –Get trained and get coached 4

5 ©2013 CliftonLarsonAllen LLP 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? 5

6 ©2013 CliftonLarsonAllen LLP 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 Duplicate purchasing and reimbursement schemes 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 6

7 ©2013 CliftonLarsonAllen LLP 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 7

8 ©2013 CliftonLarsonAllen LLP State Specific Risks Internal control overrides –Avoid purchase authority escalation –Journal entries General disbursements Payroll Derived revenues (sales tax, etc.) Beneficiary payments –Health and Human Service Grant payments 8

9 ©2013 CliftonLarsonAllen LLP State Pension Plan Brief Case Study 9

10 ©2013 CliftonLarsonAllen LLP 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 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 10

11 ©2013 CliftonLarsonAllen LLP Cash Disbursement Testing: The Monthly Report was Over 15,000 Pages 11

12 ©2013 CliftonLarsonAllen LLP Time and Effort Most Systems Provide Data in a Usable Form 12

13 ©2013 CliftonLarsonAllen LLP 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

14 ©2013 CliftonLarsonAllen LLP 14

15 ©2013 CliftonLarsonAllen LLP FY 2013 FY 2012 Benfords Analysis 15

16 ©2013 CliftonLarsonAllen LLP FY 2013 FY 2012 Stratification by Payment Amount 16

17 ©2013 CliftonLarsonAllen LLP Cash Disbursement Testing: Abnormal Payments Specific Analysis that would be difficult/impossible without Forensic Data Analysis: –Payees whose payments amounts varied significantly 17

18 ©2013 CliftonLarsonAllen LLP 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 18

19 ©2013 CliftonLarsonAllen LLP Cash Disbursement –Data Analysis vs. Traditional Procedures Data Analysis 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 19

20 ©2013 CliftonLarsonAllen LLP Consider How much data is collected in your organization? How quickly can you analyze the data for management decisions and internal risks? As Comptrollers do you believe you have your hands around all the states transactions? 20

21 ©2013 CliftonLarsonAllen LLP 21 ©2013 CliftonLarsonAllen LLP cliftonlarsonallen.com twitter.com/ CLA_CPAs facebook.com/ cliftonlarsonallen linkedin.com/company/ cliftonlarsonallen Sean M. Walker, CPA, CGFM, CGMS Managing Principal State and Local Government Ryan Merryman, CPA/CFF/CITP, CFE Senior Manager Forensic Services


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