Aug 16, 2015
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
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
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
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
What is forensic audit
•The use of accounting skills;
•To investigate frauds / embezzlement and
•To analyze financial information
•For use in legal proceedings
Forensic vis-à-vis Statutory
Forensic StatutoryVery 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
Traits of a forensic auditor
•Think out of the box
•Distrust the obvious
•Develop cognitive dissonance
•Test of absurdity
Tools available in excel
•Analyze round number transactions
•Duplicate detection
•Same, Same and different tests
•Above average payments to vendors
Tools available in excel
•Gap detection
•Automated sampling
•MATCH function
•Employee – Vendor match
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
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
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
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
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
Excel Limitations
•Absence of Log
•Not admissible in court
•Involves slight complexity in applying
•Data size limitation / Instability
•Risk of Hidden data
There are no small frauds … They just didn't get sufficient time !
THANK YOU
CA. DHRUV [email protected]