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Excel as a potent forensic accounting tool

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Excel as forensic
Auditing tool
CA. DHRUV SETH
DS@SETHSPRO.COM

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Coverage of today’s presentation
•What is Forensic audit ?
•The need of forensic auditors
•How is it different from normal...

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Name Number of Cases Amount
PNB 123 2036,00,00,000.00
CBI 174 1736,00,00,000.00
SBI 474 1327,00,00,000.00
Syndicate 114 74...

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Excel as a potent forensic accounting tool

  1. 1. Excel as forensic Auditing tool CA. DHRUV SETH DS@SETHSPRO.COM
  2. 2. Coverage of today’s presentation •What is Forensic audit ? •The need of forensic auditors •How is it different from normal audit •Traits of a good forensic auditor •Why auditors need forensic angle in all cases •Tools available in Excel •Excel Limitations
  3. 3. Name Number of Cases Amount PNB 123 2036,00,00,000.00 CBI 174 1736,00,00,000.00 SBI 474 1327,00,00,000.00 Syndicate 114 749,00,00,000.00 OBC 86 719,00,00,000.00 BOB --- 597,00,00,000.00 IDBI --- 507,00,00,000.00 UCO --- 424,00,00,000.00 United Bank --- 376,00,00,000.00 TOTAL 7542,00,00,000.00 Bank frauds – 9 months FY 2014-15
  4. 4. Need for forensic auditors •As per Association of certified fraud examiner •Each organization loses 5% of their REVENUE to fraud •Asset Misappropriation is the biggest factor •Fraud are generally NOT discovered for 18 months •Higher the fraud perpetrator BIGGER the fraud •53% frauds were by people working greater than 5 yrs •58% organizations NEVER recovered anything
  5. 5. Need for learning the traits Why frauds go unnoticed during stat audit - •extremely intelligent •Conversant with internal systems •Technology savvy •Aware of stale audit procedures
  6. 6. What is forensic audit •The use of accounting skills; •To investigate frauds / embezzlement and •To analyze financial information •For use in legal proceedings
  7. 7. Forensic vis-à-vis Statutory Forensic Statutory Very focused and micro approach Macro approach with wide coverage Examines Reliability of documentation Relies on Documentary evidences Not compulsory Regulatory compliance Establishing existence of fraud Ensuring True and fair view Determining the quantum of loss Verifying correct representations Gathering evidences Evaluating Internal Controls
  8. 8. Traits of a forensic auditor •Think out of the box •Distrust the obvious •Develop cognitive dissonance •Test of absurdity
  9. 9. Test of absurdity Think of events which may be possible but not probable.
  10. 10. Microsoft Excel as a potent forensic auditing tool
  11. 11. Why Excel •Availability •Compatibility •Powerful features •Considerable volumes of data
  12. 12. Tools available in excel •Analyze round number transactions •Duplicate detection •Same, Same and different tests •Above average payments to vendors
  13. 13. Tools available in excel •Gap detection •Automated sampling •MATCH function •Employee – Vendor match
  14. 14. Special Mention – RSF •Ratio of Largest number to the second largest number in the set RSF = Largest Number / 2nd Largest •RSF greater than 10 highlights probability of fraud / error
  15. 15. Special Mention – RSF •Types of errors / frauds it can unearth •Data Entry mistakes •Fat Finger errors •Wrong coding with masters •Capital Asset written off in expense •Excess payments in payroll
  16. 16. Special Mention – Benford’s Law •Formulated by Simon Newcomb in 1881 ; further researched by Frank Benford in 1938 •U.S. accepts Benford’s law as an evidence •Statistical tool which can be applied to normal audits also to automate samples
  17. 17. Special Mention – Benford’s Law
  18. 18. Special Mention – M-score •Theory propounded by Prof. Beneish •Stipulates the accuracy of financial statements based on certain ratios •Ratios such as •Sales to receivables and Sales Growth Index •Gross margin Index •Asset Quality Index •Depreciation Index
  19. 19. Special Mention – M-score •Financial statements score >-2.22 is considered as fudging •Statistically proven to have 76% accuracy •Model being adopted by Income Tax Department for CASS selection
  20. 20. Excel Limitations •Absence of Log •Not admissible in court •Involves slight complexity in applying •Data size limitation / Instability •Risk of Hidden data
  21. 21. There are no small frauds … They just didn't get sufficient time ! THANK YOU CA. DHRUV SETH DS@SETHSPRO.COM

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