This document discusses the use of Excel as a forensic auditing tool. It covers what forensic audits are and how they differ from normal audits. Forensic audits are used to investigate fraud and embezzlement and analyze financial information for legal proceedings. They take a more focused, micro approach compared to standard audits. The document outlines various tools available in Excel for forensic auditing, such as duplicate detection, gap detection, and ratio analysis. It provides examples of how tools like the Ratio of Largest to Second Largest and Benford's Law can help detect errors, fraud, and fudging of financial statements. Limitations of using Excel for forensic audits are also briefly discussed.

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

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. 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. 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

6. 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

7. What is forensic audit
•The use of accounting skills;
•To investigate frauds / embezzlement and
•To analyze financial information
•For use in legal proceedings

8. 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

9. Traits of a forensic auditor
•Think out of the box
•Distrust the obvious
•Develop cognitive dissonance
•Test of absurdity

10. Test of absurdity
Think of events which may be possible but
not probable.

11. Microsoft Excel as a potent
forensic auditing tool

12. Why Excel
•Availability
•Compatibility
•Powerful features
•Considerable volumes of data

13. Tools available in excel
•Analyze round number transactions
•Duplicate detection
•Same, Same and different tests
•Above average payments to vendors

14. Tools available in excel
•Gap detection
•Automated sampling
•MATCH function
•Employee – Vendor match

15. 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

16. 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

17. 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

18. Special Mention – Benford’s Law

19. 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

20. 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

21. Excel Limitations
•Absence of Log
•Not admissible in court
•Involves slight complexity in applying
•Data size limitation / Instability
•Risk of Hidden data

22. There are no small frauds
… They just didn't get
sufficient time !
THANK YOU
CA. DHRUV SETH
DS@SETHSPRO.COM