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Can You Use UAccess Analytics to Detect Fraud?

Discover how UAccess Analytics can assist in fraud detection within the Internal Audit department through real-life cases and internal control strategies.

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Can You Use UAccess Analytics to Detect Fraud?

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  1. Can You Use UAccess Analytics to Detect Fraud? Amanda Perkins and Neil Galassi Internal Audit

  2. Agenda Overview of the Internal Audit Department Quick discussion of fraud and internal controls Review two real life fraud cases to identify ways that data can (and can’t) detect fraud

  3. Department Purpose Provide independent, objective assurance and consulting services that add value and improve the operations.

  4. Who are we? • UA employees • Business Affairs

  5. What We Do • Complete audits from the Annual Audit Plan • Provide consulting services • Assist with investigations

  6. Examples of Audit Work • How is the contract monitored? • Are actual practices in compliance with contract terms? • Are there controls in place to protect data? • Are processes in compliance with UA policies? • Are payments to the contractor in compliance with contract terms?

  7. The Audit Process • Engagement Letter • Entrance Meeting • Fieldwork • Findings Discussion Sheets and Draft Audit Report • Final Report Distribution • Follow-up

  8. Ethics and Compliance Hotline (866)364-1908 compliance.arizona.edu/hotline

  9. Now Let’s Talk About Fraud!

  10. Definition of Fraud Per the Institute of Internal Auditors – Internal Auditing and Fraud Practice Guide: “Fraud is any intentional act or omission designed to deceive others, resulting in the victim suffering a loss and/or the perpetrator achieving a gain.”

  11. Fraud Facts • Fraud has many forms • Difficult to detect • Most begin small and grow • Exploit poor internal controls • Abuse authority • Auditor’s Role *Institute of Internal Auditors – Internal Auditing and Fraud Practice Guide

  12. Types of Internal Controls • Preventative • Detective • Corrective

  13. Case #1: Putting the Con in Connecticut College • Our fraudster, “KM”, oversaw student ID cards, print shop, bookstore, vending machines, transportation, laundry services, and residence halls

  14. Case #1- How Could Data Have Helped? • If you saw one of these transactions in UAccess Financials or UAccess Analytics, what would you want to know? • In a monthly review, what might have seemed out of place? • If you had access to any kind of data possible, what would you want to know?

  15. Case #1- How Could Data Have Helped? • We’ll discuss in a few minutes! Pick a spokesperson to share your best idea. • If you are joining us online- enter ideas you want to share in the question box. • You can also respond via Polling Everywhere: PollEV.com/amandaperkin649 or text AMANDAPERKIN649 to 22333 then text your response. (First 30 responses will show up.)

  16. Case #1- What Couldn’t Data Tell Us? • Segregation of duties! • Outside sources of data- for example, LLC information from the Arizona Corporation Commission • Data that isn’t ours

  17. Case #2: The Pilfering Postman • Our fraudster, “ET”, was the university’s Post Office Manager & 36 year employee • Responsible for: • Postal & Shipping Operations • Overseeing 6 employees • Equipment and Supplies Purchasing • Department Budget

  18. Case #2- How Could Data Have Helped? • If you saw one of these transactions in UAccess Financials or UAccess Analytics, what would you want to know? • In a monthly review, what might have seemed out of place? • If you had access to any kind of data possible, what would you want to know?

  19. Case #2- How Could Data Have Helped? • We’ll discuss in a few minutes! Pick a spokesperson to share your best idea. • If you are joining us online- enter ideas you want to share in the question box. • You can also respond via Polling Everywhere: PollEV.com/amandaperkin649 or text AMANDAPERKIN649 to 22333 then text your response. (First 30 responses will show up.)

  20. Case #2- What Couldn’t Data Tell Us? • Sharing login credentials- DO NOT DO IT! • Search outside data sources • Unprofessional looking invoices • Products purchased: • Not reasonable or never seen • Duplicate Items

  21. So, can data be used to detect fraud? • Sometimes! • But the other internal controls are very important, too, and work together to create a strong control environment: • Preventative • Detective • Corrective

  22. If You Want to Explore Fraud More • Fraud Documentary • All the Queen’s Horses • Netflix

  23. Ethics and Compliance Hotline (866)364-1908 compliance.arizona.edu/hotline

  24. References • Case 1: "Former Conn College employee faces 20 years for embezzlement", November 14, 2018, from TheDay.com. • Case 2: Azcentral.com article & Arizona Auditor General Report 16-405 – NAU Theft and Misuse of Public Monies, October 2016.

  25. THANK YOU! Our Contact Information can be Found at: https://audit.arizona.edu

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