Unformatted text preview: Chapter 5 Computer Fraud and Abuse Computer Learning Objectives Learning Define fraud Define Describe how fraud is perpetuated Discuss why fraud occurs Describe how to deter and detect fraud What Do These People Have in Common? What Bernie Evers, former Worldcom CEO Bernie Lea Fastow Thomas M. Coughlin, former WalMart Vice Thomas Chairman Chairman A former UH HRM dean What is Fraud? What False representation or concealment of a material fact False material By one party to another party With the intent to deceive and induce the other party With intent To justifiably rely on the material fact To rely To his or her detriment To detriment Source: Accounting Information Systems by James A. Hall Source: by What is Fraud? What (dictionary definition) Intentional perversion of the truth In order to induce someone else To part with something of value Or a legal right Types of Fraud Types Misappropriation of assets (employee fraud)
• Committed by a person or group • For personal financial gain Fraudulent financial reporting
• Intentional conduct by act or omission • That results in materially misleading financial statements Corruption
• Lack of honesty and inegrity • Use of position of trust for dishonest gain Misappropriation of Assets Misappropriation Theft of something of value
• Employee fraud usually involves the theft of assets • cash, inventory, supplies, equipment, intellectual property, cash, data data Conversion of stolen assets into cash Concealment of the crime to avoid detection detection
• • Cook the books Often leaves more evidence than the theft itself Fraudulent Financial Reporting Fraudulent Under/overstatement of revenues/assets Under/overstatement of expenses/liabilities Failure to disclose (e.g. footnotes) Corruption Corruption Bribery Kickbacks Special favors Some Common Types of Fraud Some Lapping
• • • • • • • • • • Steal cash from customer A to pay debts Use receipts from customer B to pay A Use receipts from customer C to pay B, etc. Open accounts with $1,000 in banks X, Y, and Z Deposit a $10,000 check in Y drawn on X Deposit a $10,000 check in X drawn on Z, etc. Employee pockets cash received from a customer and reduces the amount of the sale Change the amount after the check is written Change the name of the payee after the check is written Change the amount after the check is written Kiting Skimming Check tampering Common Types of Fraud Common Channel stuffing
• • To artificially boost sales at the end of a fiscal year by offering distributors and dealers To special incentives to purchase more goods than they need special "Two of the suits allege that the company failed to disclose an 'early buyout' program that "Two allowed retailers to purchase grills in November and December, keep them in Sunbeam warehouses and not pay for them until June. The allegation is that the program, sometimes called channelstuffing, masked declining grill sales." channelstuffing —David Sedore, "Five more class actions target Sunbeam," Palm Beach Daily Business —David Review Review
Employee reports higher expenses for reimbursement than actually incurred Submit receipts for expenses actually comped Employee uses assets for personal benefit Inventory theft Expense padding
• • • • Asset abuse Causes of Fraud Causes
Rationalization Opportunity Motivation Prevention & Detection of Computer Fraud Prevention Make fraud less likely to occur
• • • • • • • • Use proper employment and termination practices Train employees in fraud prevention techniques Develop a strong system of internal controls Segregate duties Fraud detection software Fraud hotline Insurance Backup copies and contingency plans Increase the difficulty of committing fraud Improve detection methods Reduce fraud losses SEE TABLE 56 FOR A MORE COMPREHENSIVE LIST SEE Benford’s Law Benford’s
0. 4 0. 35 0. 3 0. 25 0. 2 0 . 15 0. 1 0. 05 0 1 2 3 4 5 6 7 8 9 Benford’s Law Described What is the probability that the first digit in a set of multidigit numbers What (e.g., 1000 six digit numbers) is a 1? (e.g., • • • • • Between 0% and 5% 10% 11% 20% 30% Benford’s Law Described Analytical technique used to detect possible errors, Analytical potential fraud, and other irregularities potential Digits and digit sequences in a data set follow a Digits predictable pattern predictable Counts digit sequences of values in the data set and Counts compares them to totals predicted by Benford’s Law compares Predictions are based on probabilities of the Predictions appearance of the digits 1 through 9 appearance Benford’s Law History Discovered in the late 1800’s Developed by GE scientist Frank Benford in the Developed 1920’s and 1930’s 1920’s
• Logarithm books were used to multiply numbers together by adding Logarithm their common logs their • He noticed that the first few pages of logarithm books were worn He more than the last pages more • This implied that numbers started more often with the lower digits Not recognized as a fraud tool until the 1990’s Benford’s Law History Developed his theory by analyzing the first digits Developed of 20 unrelated lists of numbers (20,229 observations) observations)
• • • • • • Numbers from the front pages of newspapers All the numbers in an issued of Readers Digest All Readers Drainage area of rivers Population numbers Street numbers Baseball statistics Benford didn’t have a computer!!! Benford’s Law History Benford’s Nearly had first digits of 1 or 2 Nearly Observed actual first digit proportion patterns:
• 1: approximately the common logarithm of 2 (or 2/1) • 2: approximately the common logarithm of 3 (or 3/2) • • • • 9: approximately the common logarithm of 10 (or 10/2) 9: Benford then used integral calculus to derive the Benford expected proportions for the digits and digit combinations in tabulated data combinations Benford’s Law Math Benford’s The probability of the first digit being a “d” is P{D1 = d1} = log ((d1 + 1) / d1) Therefore, P{D1 = 1 } = Therefore, = = P{D1 = 9 } = = = log ((1 + 1) / 1) log (2) 0.30103 log ((9 + 1) / 9) log (1.11111….) 0.04576 Graphical Description Graphical
First Digit Probabilities
0. 4 0. 35 0. 3 0. 25 0. 2 0 . 15 0. 1 0. 05 0 1 2 3 4 5 6 7 8 9 Benford’s Law Math The probability of the second digit being a “d” is: P{D2 = d2} = ∑ log (1 + (1 / d1d2))
d1 = 1 9 Example: the probability that the second digit = 0
d1 1 2 3 4 5 6 7 8 9 d2 0 0 0 0 0 0 0 0 0 d1d2 10 20 30 40 50 60 70 80 90 1/d1d2 0.10000 0.05000 0.03333 0.02500 0.02000 0.01667 0.01429 0.01250 0.01111 1+1/d1d2 log(1+(1/d1d2)) 1.10000 0.04139 1.05000 0.02119 1.03333 0.01424 1.02500 0.01072 1.02000 0.00860 1.01667 0.00718 1.01429 0.00616 1.01250 0.00540 1.01111 0.00480 0.11968 Benford’s Law Benford’s
Expected Digit Frequencies Practical Application Someone fabricating numbers to commit fraud Someone usually doesn’t follow the Benford’s Law pattern usually The first accounting application (Carslaw, 1988) The found a tendency for upward rounding of net income: found
• a predominance of second digit 0s • a shortage of second digit 9s Dorell’s 2002 analysis of fraudulent payroll records:
• 0 turned up twice as often as predicted • 5 turned up 60% more often than predicted Requirements
The numbers in the data set The should describe the sizes of similar phenomena (e.g., should payroll, revenues) payroll, should have no builtin maximums or minimums should (e.g., expense account reimbursement limits, labor hours) hours) The numbers should not be assigned numbers used to The name elements in a data set (e.g., drivers license numbers) numbers) CNBC Report: Channel Stuffing CNBC CNBC Report: Channel Stuffing CNBC END OF CHAPTER 5 END ...
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This note was uploaded on 10/02/2010 for the course ACCT 5457 taught by Professor Polm during the Fall '10 term at Rensselaer Polytechnic Institute.
 Fall '10
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