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Practical Implementation of Data Analysis IIA OC Chapter Meeting February 18, 2014 Edwards Lifesciences Global Internal Audit Agenda Introduction Data Mining Travel and Expense Audit Special Words Stratification


  1. Practical Implementation of Data Analysis IIA OC Chapter Meeting February 18, 2014 Edwards Lifesciences Global Internal Audit

  2. Agenda • Introduction • Data Mining – Travel and Expense Audit • Special Words • Stratification – Cash vs. Credit Card • Excel Risk and Validation – Best practices – Spreadsheet Validation Demonstrations

  3. Global Internal Audit Team • Abel Casanova, Sr. Manager • Orlando Lopez, Sr. Manager • Daphne Chi, Sr. Auditor • Julie Ann Fan, Auditor

  4. Data Mining – What is Data Mining? • Automated or semi-automated analysis which transfers large-scale data into understandable information • Using computer tools to examine all (or nearly all) the population in an audit test – Why Data Mine? • Data mining is a fast and efficient way to reveal hidden exceptions, patterns, and trends

  5. Data Mining Example: Expense Report Analysis Allan Capone Jr. – Sales Representative based in Las Vegas, Nevada – Expense reports from 2011-2013 • Obtained extract of data from the T&E System • 2,125 transactions over 36 months

  6. Data Mining Audit Test – Special Words – What are “Special Words”? • “Special Words” is a type of audit test used to identify transactions with suspicious words • “Special Words” is an audit test used to analyze the “grey” areas and is a type of fuzzy logic – Why use Fuzzy Logic? • Incorporates reasoning in the audit test to identify fraud or exceptions • Can find really neat items

  7. Data Mining Audit Test – Special Words • Golf, mini, bar, cash, advance, gift, bribe, adjustment, allocation, government, party, cell phone, event, service – Additional fun words: Jack Daniels • Using Caseware IDEA, the @Isini function searches for the occurrence of a specified string or piece of text in a character field, date field, or string and if found it returns the starting character position of the specified string. If the string is not found, a value of 0 is returned. @Isini ( "Golf" ,COMMENT) .OR. @Isini( "mini" , COMMENT) .OR. @Isini( "bar" , COMMENT) .OR. @Isini( "jack" , COMMENT) .OR. @Isini( "cash" ,COMMENT) .OR. @Isini( "advance" , COMMENT) .OR. @Isini( "gift" , COMMENT) .OR. @Isini( "bribe" , COMMENT) .OR. @Isini( "allocation" ,COMMENT) .OR. @Isini( "adjustment" , COMMENT) .OR. @Isini( "event" , COMMENT) .OR. @Isini( "adjust" ,COMMENT) .OR. @Isini( "party" , COMMENT) .OR. @Isini( "service" , COMMENT) .OR. @Isini( "government" , COMMENT) .OR. @isini("cellphone", COMMENT)

  8. Data Mining Audit Test – Special Words

  9. Data Mining Audit Test – Special Words Results Golf, mini, bar, cash, advance, gift, bribe, adjustment, allocation, government, party, cell phone, event, service, Jack Daniels

  10. Data Mining Audit Test – Special Words – What did we do? • Used logical reasoning and applied the special words test – What did we find? • Allan Capone Jr. has charged non-compliant transactions to the company card – What can Special Words be used for? • Travel & Expense Reports • Journal Entries • Accounts Payable – Tip • Consider acronyms, foreign languages or ask local speakers for slang

  11. Data Mining Audit Test – Stratification – What is Stratification? • Stratification is the process of dividing the population into subgroups before sampling – Why use Stratification? • Narrows down large data into subgroups and can provide information by dollar amount, type of transaction or transaction frequency • Conclusions or analysis may be easier to apply to population subgroups • Good way to begin sampling

  12. Data Mining Audit Test – Stratification • Stratify by Amounts – $0 - $20 – $20 - $45 – $45 - $100 – $100 - $1,000 – $1,000 - $2,000 – $2,000 - $5,000 • Stratify by transaction line amounts

  13. Data Mining Audit Test – Stratification

  14. Data Mining Audit Test – Stratification • Why would a sales representative need to charge amounts over $1,000? • Investigate!

  15. Data Mining Audit Test – Stratification • Why do these flights cost so much? – First Class/ Business Class flights are not allowed under Corporate Policy – Possible multiple changes to flights?/ Last minute flights? – Paying for miles?

  16. Data Mining Audit Test – Stratification • Stratification - Cash vs. Card – $0 - $10 – $10 - $20 – $20 - $25 – $25 - $50 – $50 - $100 – $100 - $1,000 – $1,000 - $5,000 • Now we look at smaller stratus because the last stratus only indicated possible exceptions over $1,000 but we should try and investigate exceptions under $1,000.

  17. Data Mining Audit Test – Stratification Results • Analyze the Results • Look for patterns • Do the results seem reasonable?

  18. Data Mining Audit Test – Stratification Comparison 2/18/2014 18

  19. Data Mining Audit Test – Stratification – What did we do? • We used the stratification to narrow down the transactions into smaller samples – What did we find? • Exceptions to T&E policy (i.e., Business class seats and suspicious cash transaction) – What can stratifications be used for? • AR Aging • AP Aging – Tip • Stratifications are the beginning of audit analysis not the end

  20. Data Mining Audit Test – Policy Compliance – What is stratification sub-group analysis? • Taking the next step data stratification • One can build audit rules surrounding company policy – Why analyze stratification sub-groups? • Detects non-compliance transactions to corporate policy • GREAT practical application of data analysis

  21. Data Mining Audit Test – Policy Compliance • Company T&E Policy - Cash – Cash transactions under $25 do not require receipt – Cash transactions over $20 but less than $25 AMOUNT > 20 .AND. AMOUNT < 25 – 246 cash transactions were between $20 & $25

  22. Data Mining Audit Test – Policy Compliance - $21.38 at heartbeat café is curious activity. -taxi and valet parking in the same report is suspicious - American Airlines baggage fee is $25 and requires a receipt under company policy

  23. Data Mining Audit Test – Policy Compliance • What did we do? – Reviewed transactions between $20-$25 • What did we find? – Fake/non-compliant charges to T&E • What can policy compliance test be used for – Approval Thresholds – Willful bypass • Tip – This can be used for many company policies. Be creative!

  24. Allan Capone Jr.

  25. Spreadsheet Risks and Validation • Our Excel Journey – Only doing the minimum on spreadsheet controls – More COSO/SOX demands were on the way – Wanted to bring more audit and control value • Spreadsheet risks • Best Practices • Spreadsheet Validation Demonstration

  26. Our Excel Journey • We still use many spreadsheets • Those spreadsheets have risks • Select best practices were implemented • Needed easy-to-use validation tool • Selected the following Excel add-on tools by Incisive: – Xcellerator – Diff Interactive

  27. Common Use of Spreadsheets • Income Statement Fluctuation Analysis • Accounts Receivable Reserve Analysis • Excess & Obsolete Inventory Reserve • Vacation Accrual Update • Rebates Calculation • Outstanding Shares Calculation • Stock Repurchase Daily Summary • Statement of Cash Flow Calculation

  28. Spreadsheet Risks • Unauthorized changes to the data by users • Hidden worksheets or cells • Formula overwritten with text or numbers • Formula fails to cover full area • Incorrect referencing • Calculations are not refreshed

  29. Best Practices • Inventory and risk rank your spreadsheets based on complexity and potential impact • Set security access levels for authorized users • Establish version control and restrict changes to formulas • Secure key spreadsheets on servers for backup purposes and security • Routinely review spreadsheets for key changes

  30. Spreadsheet Validation Tools • Validation tools by Incisive: – Xcellerator : add-in application created for use with Microsoft Excel. This application scans spreadsheets for likely errors and inconsistencies, allowing users to find and fix errors before they become problems. – Diff Interactive : spreadsheet comparison software to quickly determine if and/or what spreadsheet changes were made by users.

  31. Demonstration Background Assumptions: • O&D Corporation is a medical device manufacturing company • Business model: Direct Customers or Distributors • 2Q2013 vs. 1Q2013 AR Reserve Analysis

  32. Demonstration of Xcellerator • What will we do? – Analyze an AR aging report and calculation of the allowance for doubtful accounts • Examples of kinds of tests will we run: – “#” Errors and source – Data in formula range – Constant in formula (e.g., hardcoded numbers inside a formula) – Hidden Worksheets/Cells/References – External workbook references

  33. Demonstration Part I - Xcellerator

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