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Using Data Analytics as a Management Tool to Identify Organizational Risks

Using Data Analytics as a Management Tool to Identify Organizational Risks. Sean Walker, Managing Principal Ryan Merryman, Senior Manager. Objectives. Discuss how data analytics can be used to better identify various risks in an organization.

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Using Data Analytics as a Management Tool to Identify Organizational Risks

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  1. 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

  7. Types of Risks and Areas of Analysis

  8. 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

  9. State Pension Plan Brief Case Study

  10. 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. 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

  14. Benford’s Analysis FY 2013 FY 2012

  15. Stratification by Payment Amount FY 2013 FY 2012

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

  17. 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. Cash Disbursement –Data Analysis vs. Traditional Procedures

  19. 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?

  20. Sean M. Walker, CPA, CGFM, CGMS Managing Principal State and Local Government 414-721-7521 Ryan Merryman, CPA/CFF/CITP, CFE Senior Manager Forensic Services 310-292-9867

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