1. DATA MINING
&
FORENSIC AUDIT
By
Dhruv Seth
ds@sethspro.com | www.sethspro.com

2. CONTENT
• Data Mining
• Methods of doing
• Difference with standard auditing
• Benefits and Risks
• Patterns in data
• Utilisation in different audits
• Forensic Audit
• What is a fraud
• Profile of a fraudster
• Tools available in excel
• Theorems

3. A PROBLEM…
•A large retail chain doing substantially well
had
•Dismal diaper sale ; Excellent Beer sale
SOLUTION
Place them together !

4. WHICH IS SUSPICIOUS ?
• User 1: Login → Click on Product #8473 → Click
on Product #157 → Click on Product #102 →
Complete Purchase
• User 2: Failed Login → Request Password →
Direct Link to Product #821 → Change Shipping
Address →Complete Purchase

5. Data Mining
Computer Expertise
≠

6. WHAT IS DATA MINING
Data
Information

7. IDENTIFYING SUSPICIOUS TRANSACTIONS
Computer Behavioral Smartphone Analytics
Mouse Dynamics Screen Pressure
Typing Speed Angle of usage of phone
Previous Navigation
Habits
Movement across screen
Entry & Exit points on
website
Heart Rate

8. DATA MINING - VALUE ADDITION
What was my total revenue in the last five years?
TO
What were sales in UP last March? Drill down to Kanpur
TO
What’s likely to happen to Kanpur sales next month? Why?

9. DATA MINING - METHODS
•Association
•Sequence or path analysis
•Classification
•Clustering
•Prediction

10. DATA MINING - TECHNIQUES
•Artificial neural networks
•Decision trees
•The nearest neighbour method

11. DATA MINING V. REGULAR AUDIT
Labor Verification
Regular Audit Data Mining
1. Contracted rate =
Billing rate
1. Contracted rate = Billing rate
2. The billing is relevant to
the audit period.
2. Employee Pay grade wise payment
3. Statutory Compliances 3. Mapping resignation to Last Pay
4. Mapping computer / biometric logins
after resignation / termination
5. Overtime Analysis to determine
a.) Regular Overtime
b.) Employees who worked 100 hrs
6. Those not availing leaves

12. DATA MINING - STEPS
•Business Understanding
•Data Understanding
•Data Preparation
•Data Modelling
•Evaluation
•Deployment

13. DATA MINING – WHY INTEGRATE
•Transaction Volume
•Mitigate Inherent Risk
•Value addition to the client
•Cost Effective

14. DATA MINING – BENEFITS
•Remove Sampling risk – 100% coverage
•Decrease in Audit costs
•Provide Real time audit opinions
•Establish Completeness and accuracy

15. DATA MINING – SOFTWARE TYPES
•Generalized Software
•Specialized Software

16. DATA MINING – SOFTWARE TYPES
Characteristics Generalised Specialised
Batch Processing No Yes
Support entire audit procedures No Yes
User friendly Yes No
Require technical skill No Yes
Automated No Yes
Capable of learning No Yes
Cost Lower Higher

17. DATA MINING – RISKS
•First year costs might be higher
•Strong understanding of operations
•Availability of data in desired format
•Risk of Control totals

18. DATA MINING – PATTERNS
•Numeric Patterns
•Time Patterns
•Name Patterns
•Geographical Patterns
•Relationship Patterns
•Textual Patterns

19. • Purchases
• Vendors and accounts payable
• Employees and payroll
• Expense reimbursement
• Credit Card utilisation
• Sales & Debtors
• Inventory
• Commission Payouts
DATA MINING – INTERNAL AUDITS

20. PURCHASES
•Round number transactions
•Duplicate transactions
•Same, Same, Different Test
•Above average payments
•Transactions exceeding PO quantity
•Sequential Invoice numbers
•Too many invoices beginning with “9”
DATA MINING – INTERNAL AUDITS

21. CREDITORS
•Those with high percentage of returns
•Those with rapid increasing purchases
•Small denomination but quick frequency
•SOD for vendor approver and purchaser
DATA MINING – INTERNAL AUDITS

22. PAYMENT TREND ANALYSIS
By the day of week
By the day of Month
DATA MINING – INTERNAL AUDITS

23. DATA MINING – EXPENSES

24. VENDORS MASTER
• Analysis of Vendors master for creation date
• Identifying regular prompt vendor payment
• Cross reference vendors to employees
• Same, Same and Different test
DATA MINING – INTERNAL AUDITS

25. EMPLOYEES AND PAYROLL
• Regularly working overtime
• Not taking leaves
• Satisfied with unjustified salary deduction
• Segregating employees with salary in cash
• Biometric analysis – First to enter / last to leave
DATA MINING – INTERNAL AUDITS

26. TRAVEL EXPENSES
• Identify weekend or holiday travel
• Search for same or similar claims
• Identify costs outside of policy or costly late bookings
• Identify conveyance claim made for the same time period
as car rental or other transportation
• Compare mileage claims to distances reported
• Instances where employee has refunded a first class
ticket for an economy, but not reimbursed the balance
back to the company.
DATA MINING – INTERNAL AUDITS

27. SALES & DEBTORS
• Comparing Invoice to Shipping
• Conversely comparing Shipping to Invoice
• Preference in sale to a particular customer
• Same, Same, Different test to sale price
• Debtors
• Lapping
• Old outstanding invoices
DATA MINING – INTERNAL AUDITS

28. INVENTORY
• Determining slow moving inventory
• Determining quick moving inventory
• Purchasing frequency of a particular product
• Mapping stock valuation to last sale price
DATA MINING – INTERNAL AUDITS

29. • Transactions a customer does before shifting? (to
prevent attrition)
• Profile of an ATM customer and what type of
products is he likely to buy? (to cross sell)
• Patterns in credit transactions lead to fraud? (to
detect and deter fraud)
• Traits of a high-risk borrower? (to prevent
defaults, bad loans, and improve screening)
DATA MINING – BANKS

30. • Duplicate Customer id
• DP Limit = Limit = Outstanding
• Comparing Unsecured and secured within scheme
• Rate of Interest being applied
• Last Credit amount and Date
• Same PAN – Different Customer id
• Last Stock statement summary
DATA MINING – BANKS

31. •Rubbing Nose
•Frequent blinking
•Moving or Tapping feet
•Crossing Arms
•Clearing throat
•Pinched eyebrows
•Smirk
DATA MINING – BEHAVIOR

32. FORENSIC AUDIT

33. REPORT TO THE NATION
• 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
• 58% organizations NEVER recovered anything

34. FRAUD DETECTION

35. BANK FRAUDS – 9 MONTHS FY 2014-15
Name Number of Cases Amount
PNB 123 2036,00,00,000
CBI 174 1736,00,00,000
SBI 474 1327,00,00,000
Syndicate 114 749,00,00,000
OBC 86 719,00,00,000
BOB --- 597,00,00,000
IDBI --- 507,00,00,000
UCO --- 424,00,00,000
United Bank --- 376,00,00,000
TOTAL 7542,00,00,000

36. • A false representation of a matter of fact
• whether by words or by conduct,
• by false or misleading allegations, or
• By concealment of what should have been
disclosed
• that deceives and is intended to deceive another
• so that the individual will act upon it to her or his
legal injury.
WHAT IS FRAUD ?

37. FRAUD TRIANGLE

38. WHAT IS FORENSIC AUDIT
•The use of accounting skills;
•To investigate frauds / embezzlement and
•To analyze financial information
•For use in legal proceedings

39. 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

40. GHOST EMPLOYEES

41. 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

42. FRAUDSTERS PROFILE
• Flamboyant lifestyle
• Very aggressive in his approach / targets
• Over protectiveness of data / documents
• Being the first one in and last one out
• Unusual close association with vendor / customers

43. FRAUDSTERS PROFILE

44. FRAUDSTERS PROFILE

45. FORENSIC AUDITOR
Forensic
Accountant
Law
Accounting
Criminology
Investigative
Auditing
Computer
Science

46. TRAITS OF A FORENSIC AUDITOR
•Think out of the box
•Distrust the obvious
•Develop cognitive dissonance
•Test of absurdity

47. TEST OF ABSURDITY
Think of events which may be possible but
not probable.

48. TOOLS AVAILABLE IN EXCEL
•Analyze round number transactions
•Duplicate detection
•Same, Same and different tests
•Above average payments to vendors

49. TOOLS AVAILABLE IN EXCEL
•Gap detection
•Automated sampling
•MATCH function
•Employee – Vendor match

50. SPECIAL MENTION – TIME & SPACE
•Establish transactions in quick successions
which take a substantial time in happening
•Storage in excess of the possible space

51. 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

52. 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

53. 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

54. SPECIAL MENTION – BENFORD’S
LAW

55. 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

56. 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

57. EXCEL LIMITATIONS
•Absence of Log
•Not admissible in court
•Involves slight complexity in applying
•Data size limitation / Instability
•Risk of Hidden data

58. THANK YOU !
By
Dhruv Seth
ds@sethspro.com | www.sethspro.com