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Data mining and Forensic Audit

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Kanpur Chartered Accountants' Society residential refresher course at Jim Corbett 2016.

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Data mining and Forensic Audit

  1. 1. DATA MINING & FORENSIC AUDIT By Dhruv Seth ds@sethspro.com | www.sethspro.com
  2. 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. 3. A PROBLEM… •A large retail chain doing substantially well had •Dismal diaper sale ; Excellent Beer sale SOLUTION Place them together !
  4. 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. 5. Data Mining Computer Expertise ≠
  6. 6. WHAT IS DATA MINING Data Information
  7. 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. 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. 9. DATA MINING - METHODS •Association •Sequence or path analysis •Classification •Clustering •Prediction
  10. 10. DATA MINING - TECHNIQUES •Artificial neural networks •Decision trees •The nearest neighbour method
  11. 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. 12. DATA MINING - STEPS •Business Understanding •Data Understanding •Data Preparation •Data Modelling •Evaluation •Deployment
  13. 13. DATA MINING – WHY INTEGRATE •Transaction Volume •Mitigate Inherent Risk •Value addition to the client •Cost Effective
  14. 14. DATA MINING – BENEFITS •Remove Sampling risk – 100% coverage •Decrease in Audit costs •Provide Real time audit opinions •Establish Completeness and accuracy
  15. 15. DATA MINING – SOFTWARE TYPES •Generalized Software •Specialized Software
  16. 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. 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. 18. DATA MINING – PATTERNS •Numeric Patterns •Time Patterns •Name Patterns •Geographical Patterns •Relationship Patterns •Textual Patterns
  19. 19. • Purchases • Vendors and accounts payable • Employees and payroll • Expense reimbursement • Credit Card utilisation • Sales & Debtors • Inventory • Commission Payouts DATA MINING – INTERNAL AUDITS
  20. 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. 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. 22. PAYMENT TREND ANALYSIS By the day of week By the day of Month DATA MINING – INTERNAL AUDITS
  23. 23. DATA MINING – EXPENSES
  24. 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. 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. 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. 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. 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. 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. 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. 31. •Rubbing Nose •Frequent blinking •Moving or Tapping feet •Crossing Arms •Clearing throat •Pinched eyebrows •Smirk DATA MINING – BEHAVIOR
  32. 32. FORENSIC AUDIT
  33. 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. 34. FRAUD DETECTION
  35. 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. 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. 37. FRAUD TRIANGLE
  38. 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. 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. 40. GHOST EMPLOYEES
  41. 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. 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. 43. FRAUDSTERS PROFILE
  44. 44. FRAUDSTERS PROFILE
  45. 45. FORENSIC AUDITOR Forensic Accountant Law Accounting Criminology Investigative Auditing Computer Science
  46. 46. TRAITS OF A FORENSIC AUDITOR •Think out of the box •Distrust the obvious •Develop cognitive dissonance •Test of absurdity
  47. 47. TEST OF ABSURDITY Think of events which may be possible but not probable.
  48. 48. TOOLS AVAILABLE IN EXCEL •Analyze round number transactions •Duplicate detection •Same, Same and different tests •Above average payments to vendors
  49. 49. TOOLS AVAILABLE IN EXCEL •Gap detection •Automated sampling •MATCH function •Employee – Vendor match
  50. 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. 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. 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. 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. 54. SPECIAL MENTION – BENFORD’S LAW
  55. 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. 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. 57. EXCEL LIMITATIONS •Absence of Log •Not admissible in court •Involves slight complexity in applying •Data size limitation / Instability •Risk of Hidden data
  58. 58. THANK YOU ! By Dhruv Seth ds@sethspro.com | www.sethspro.com

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