Fraud Detection & Data Analytics

Transcription

1 September 2008 Fraud Detection & Data Analytics How to Utilize CAATs to detect fraud AN INDEPENDENT MEMBER OF BAKER TILLY INTERNATIONAL 505 AFFILIATE OFFICES WORLDWIDE

2 Course Topics Overview of Data Analytics Accounts Receivable Overtime Audit Inventory / Obsolescence Commissions Payroll Sales Purchasing Telephone Charges Regression Analysis Credit Card Purchases No Vacancy p 4-8 p 9-11 p p p p p 24 p 25 p p 34 p p

3 Course Topics Digital Analysis Benford s Law Signing Authority Ratio Analysis Contracting Kickbacks Doctored Bills Time and Expenses Reference Material Appendix p 40 p p 43 p p p p 51 p 52 p

4 Importance of Data Requirements Request Incorrect statement of audit data requirements can result in information received not being what was needed Failure to identify important fields requiring the auditors to ask for a second and third data extraction Obtaining the information in a format that is not conducive to further electronic use (Monarch) Failing to tell the client the file format for the requested data file (ASCII flat file,.csv delimited, dbase, fixed record length) 4

5 Incorrect Identification of Audit Population Important to pull all necessary accounts ACL reference (pg 151) 5

6 Data Analytics. Risk of Making an Error When extracting data it is critical to ensure the decimal place is in the correct place Risk: potential incorrect definition of the format file The incorrect use of the join function when combining two or more files Auditors have used duplicate files thereby double counting all transactions 6

7 Fraud Application Analyze the control framework to identify areas of greatest risk Examine possible risks including those in Consideration of Fraud in a Financial Statement, SAS#82 and Deterrence, Detection, Investigation and Reporting of Fraud, SAS #3 Identify the symptoms related to these risks How would possible fraud be reflected in the data? 7

8 Proactive Analysis Batch jobs Develop batch jobs to test for symptoms of fraud Proactively looking for fraud symptoms means auditors can detect and even prevent the fraud i.e. payroll batch test example It is possible to perform 100% examination of data Determine a frequency for running the jobs Run the jobs and review the results 8

9 Accounts Receivable To compensate for her meager salary, an A/R clerk started cashing customer payments When customers paid by check, she used one of two methods: 1) Take the customer check, cash it, and don t record the payment. In this case, she would create a new customer monthly statement to make it look like the payment had been credited to the account. The account balance would still show an account balance higher than the balance on the statement, but the customer would not know that 9

10 Accounts Receivable (Cont d) 2) Cash the customer s check and hide the theft by crediting the customer account, meanwhile debiting a fictitious account she had set-up. This meant that her receipts balanced, and the valid customer accounts were correct. Later, she would write-off the fictitious account as bad debt The company had established a voice system that customers could call to get their balances, and the customers were reporting discrepancies 10

11 Accounts Receivable An auditor had looked at trends in the A/R department and determined that the amount written off as bad debt had increased significantly in the last year An aging on the accounts that had been written off was performed The results showed that some accounts had been written off even though the balance had only been outstanding for 3 months The usual period was one year and only after three reminders had been sent out Not one reminder letter had been sent out 11

12 Overtime Audit Identify Managers with overtime expenditures greater than 1.5 times last year s overtime totals Highlight all managers with overtime expenditures greater than or equal to 10% of their regular pay budget Identify all employees with total overtime payments equal to more than 25% of their salary 12

13 Overtime Audit The overtime can be analyzed by type: Regular overtime, 1 st day of rest -Saturday 2 nd day of rest Sunday By matching the overtime records to the attendance system, audit can determine if individuals are consistently taking the next 2 days off with or without pay Total overtime paid by type of overtime was calculated to determine instances where individuals were working more overtime on the second day of rest at double time rates vs. regular overtime (at time and a half) This highlights where management controls over the use of OT might not be working as intended or effective ACL reference (pg 167) 13

14 Using ACL to Identify Obsolete Inventory Items Reducing inventory carrying costs Raw materials / sub-components should be deemed obsolete when the parent finished good product is declared obsolete (in some cases) Where used reports can be utilized to determine where a raw material is used within finished good products Items used to maintain the obsolete item should be declared obsolete; unless items supported another piece of equipment 14

15 Obsolete Identification Example: Auditors obtained a file of obsolete items for the past three years; compared those items to an active inventory listing and subsequently. Flagged those items that supported obsolete pieces of equipment Secondarily, they flagged those items marked obsolete to see if they supported other non-obsolete pieces of equipment ACL reference (pg. 118) 15

16 Inventory Levels Identify inventory items with stock levels greater than maximum stock levels Stock levels on hand that would satisfy several years worth of use Low turnover rates Check for shelf life flags extract items which have been stored for periods longer than stated shelf life Compare turnover rates with reorder levels (Example: Does it make sense to set reorder qty at 100 if it took five years to order 25 items) 16

17 Inventory Levels (cont d) Compare turnover rate by item by location Run statistics to determine negative inventory reporting Timing Shipping reports product shipped before manufacturing floor reports product as produced Partial shipments risk of backflushing full order Trend analysis on inventory shrinkage (avg. price, yearly loss, etc.) Search for inventory at zero standard cost Search for finished goods standard cost that does not equal sum of components 17

18 Commissions / Bonus Review policies and procedures to understand commission schedules and conditions for bonuses. Extract data from commission system for past five years to determine commission and bonus payment pattern Reviewing the total of these payments for the past five years by region allow auditors to identify trends and determine regions with largest total payments for commissions / payments ACL reference (pg 108 B) 18

19 Commissions Identify all employees by geographical area who had received commissions amounting to 20% or more of their regular salary figures Identify all locations with significant increases over last year s budgets for commissions / bonuses Totals by location, sales area, and other criteria can be reviewed These results are used to select a sample 19

20 Commissions Select the job title, work location, supervisor and rate of pay from the personnel system The compensation system is used to determine total commissions and bonuses paid to each employee in the sample (for current year and past three years) The sales system was used to identify the annual sales volume for these employees for the past three years Note: An analysis of returns after quarter-end by responsible salesperson could be performed as well 20

21 Payroll Fraud Symptoms Risk of continuing to pay ex-employees Risk of paying new employees prior to their actual start date Risk increases with rapid growth, high turnover Compare the personnel system to the payroll system This batch job can be run before checks are released Thereby, erroneous checks can be identified ACL reference Pg

22 Payroll Test for Duplicate Direct Deposit Numbers Test for Duplicate Employee Addresses Example: Developer responsible for the benefits system was able to create three ghost employees and direct their pay to his bank account Compare payroll disbursements to employee master file to identify discrepancies 22

23 Payroll Analytics Direct deposit number duplicate No medical deductions No tax deductions Employee name duplicate Employee address duplicate Employee / vendor addresses match Overtime pay greater than X amount SSN blank SSN duplicate Check sequence Duplicate payments 23

24 Sales Numerous transactions at multiple store locations requires data analytics to track Discover improper discount or promotional applications Detect trends in pricing, inventory by store or region Trace sales to valid vendors Ensure validity of vendors through approval tracking and duplicate testing on vendor master file this can prevent duplicate, fraudulent, and miscoded transactions ACL Reference(pg. 15)

25 Purchasing Fraud and Detection Fictitious vendors Filtering P.O accounts, blank fields Join function to search matches in vendor and employee addresses, phone numbers Duplicate, similar and generic vendor searches Identifying knock off vendors through sounds-like function Sequential invoices Gap analysis Test for duplicates on date and invoice number

26 Telephone Charges Telecommunication charges were increasing steadily The data received from the phone company included: the originating phone number phone number called date and time of call length of call in minutes cost ACL was used to run several reports, including: All long distance calls longer than three hours The results showed a number of calls that were exactly 999 minutes (over 16 hours) in length 26

27 Telephone Charges (Cont d) By performing a detailed review of the activity on these lines the auditors found that other calls had been made during this time period on phones that did not enable multiple calls The root cause after checking with the phone company was a faulty communication switch that remained open after the phone had been hung up Failing to register the completion of the call, thereby resulting in an erroneous long distance charge 27

28 Telephone Charges (Cont d) The phone company had a maximum call length of 999 minutes or the charges would have been even higher All charges were reversed by the company Overall result was 17% reduction in the total telecommunications bill 28

29 Telephone Charges (Cont d) In some cases, where calls were longer than 180 minutes, large data transfers were being performed between two sites. The auditors summarized the detailed billing information and determined that usage was high enough to justify leasing a dedicated line Reducing the overall cost and improving the transmission speeds and reliability of file transfers. 29

30 Telephone Charges (Cont d) Another test centered on possible abuse of long distance privileges Auditors identified calls after working hours or during holiday periods Recommendations: Restrict the ability to call long distance after 6pm and on weekends and holidays A simple change to the company s telecommunication switch software to block all access to pay-per-minute exchanges (search data for 1-900, calls) 30

31 Telephone Charges (Cont d) The audit reviewed the accuracy of the telephone bill and the efficiency and effectiveness of leased lines The auditors selected a sample of dedicated leased long distance lines from a number of branch offices Automatically generated confirm letters to be sent to the branch offices The letter asked the branch managers to verify the accuracy of the charges by ensuring the lines were still connected Managers were asked to justify the lines 31

32 Telephone Charges (Cont d) In 10% of the cases, the lines were no longer required but the service had never been canceled In a further 5% of the cases, the lines were not even physically connected to a phone. Because of office redesigns, telephone lines terminated in closets, etc. 32

33 Telephone Charges (Cont d) The use of audit software to analyze thousands of lines of detailed calling information, and to highlight anomalies or potential abuses, greatly improved the effectiveness of the audit Overall result was 17% reduction in the total telecommunications bill 33

34 Regression Analysis Regression Analysis is a statistical method for examining a series of records to determine if the values are appropriate A mathematical relationship is established between the driver and the dependent variable For example, the amount of heating fuel used is dependent on the temperature Using regression analysis you can estimate how much heating fuel will be used given a certain temperature Auditors and fraud investigators can compare predicted values with actual values 34

35 Credit Card Purchases The bank was losing millions of dollars due to fraudulent purchases made using stolen credit cards The audit director was asked to identify a way to identify inappropriate purchases as early as possible Regression analysis was used to compare each transaction per cardholder to their normal purchasing pattern Anomalies were identified and cardholders called to determine if they had made the purchase 35

36 Credit Card Purchases A second analysis was to review vendors A third analysis was a comparison of the dates and locations of purchases Two purchases made on the same day, but in widely separated zip codes would cause a flag to be set 36

37 No Vacancy The manager of a large apartment complex in the city claimed he had a 5% to 10% vacancy rate The rate was not significantly higher than any of the other buildings owned by the company The manager was renting out apartments to friends and relatives, without having them sign a lease They paid him a nominal sum each month and he kept the cash Since they did not sign the lease, the owners of the complex thought the apartments were empty 37

38 No Vacancy The auditors were reviewing the leases for all the buildings owned by the company They had enough data to use regression analysis to predict the number of people living in a building Based on electricity, water, and heat usage The predicted number of tenants was then compared with the actual number of people listed on the leases Everything checked out within acceptable limits until they came to this complex 38

39 No Vacancy The analysis estimated that there should be 1,203 tenants, but the leases only accounted for 1,147 A careful review of all apartments found 58 people living in 14 apartments that the manager had rented to his friends and relatives, without a signed lease 39

40 Digital Analysis A growing area of fraud prevention and detection involves the examination of patterns in the actual data The rationale is that unexpected patterns can be symptoms of possible fraud A simple example of the application of this technique is the search for duplicate transactions, such as the same invoice number and vendor number 40

41 Benford s Law More advanced techniques take data analysis to another level, examining the actual frequency of digits in the data Benford s Law developed by Frank Benford in the 1920 s makes predictions on the occurrence of digits in the data Benford s Law predicts that the first digit in a large number of transactions (10,000 plus) will be a 1 more often than a 2 ; and a 2 more often than a 3

42 Benford s Law Digit Frequency (first digit) Frequency (second digit)

43 Signing Authority Analysis of contracts showed the digits 49 were in the data more often than expected Classifying on the Contracting Officer for all contracts with 49 determined that a contracting manager was raising contracts for $49,999 Contracts under $50,000 could be sole sourced; Contracts $50,000 or higher had to be submitted to a bidding process He was raising contracts just under the financial limit and directing them to a company owned by his wife

44 Ratio Analysis A useful fraud detection technique is the calculation of ratios for key numeric fields Data analysis ratios point to possible symptoms of fraud Commonly used ratios are: The ratio of highest value to the lowest value (maximum / minimum) The ratio of the highest value to the next highest (maximum / 2 nd highest) The ratio of the previous year to the current year 44

45 Ratio Analysis For example, auditors concerned about prices charged for a particular product Calculate the ratio of the maximum sales price to the minimum sales price If the ratio is close to one, they can be sure that there is not much variation between the highest and lowest prices charged to customers If the ratio is large, this could be an indication that a customer is being charged too much or too little 45

46 Contracting Kickbacks A contract officer had devised a scheme The auditors used digital analysis as part of their review One analysis calculated the total contract amount by vendor for each of the two past years A ratio of current year to previous year was calculated and the STATISTICS command was used to look at the following: Minimum Maximum Average Highest five ratios Lowest five ratios

47 Contracting Kickbacks (Cont d) The highest 5 ratios and the lowest 5 ratios showed that some companies had significant decreases in business, while others had significant increases The auditors reviewed the details for all companies with last year to current year ratio of less than 0.7 or greater than 1.3; The detailed records were extracted to a file Totals were calculated by contracting officer

48 Contracting Kickbacks (Cont d) One contracting officer was responsible for many of the companies that had seen an increase in business; However, the same contracting officer had raised no contracts with the companies that had seen a decrease in business; By talking to the salespeople at these companies, the scheme was uncovered

49 Doctored Bills The auditors reviewed the patient billing system to determine if the appropriate charges were being assessed to the patient s healthcare providers An initial analysis of the data was performed to calculate the ratio of the highest and lowest charges for each procedure The auditing standards required that procedures with a ratio of highest to lowest greater than 1.3 be noted and additional review performed Three procedures had ratios greater than 1.30

50 Doctored Bills (Cont d) A filter was set to identify the records related to the three procedures in question and additional analysis was performed This quickly determined that one doctor was charging significantly more than the other doctors for the same procedures A comparison of the charges from the billing system with the payments recorded in the accounts receivable system, revealed that the doctor was skimming a portion of the payment received

51 Doctored Bills (Cont d) The amount recorded in the receivable system was in line with the usual billing amount for the procedures The doctor was unable to justify the higher prices or explain the difference in the billing and the receivable systems

52 Time and Expenses In ACL auditors use the MOD function to identify transactions that are multiples of $10 or multiples of $100 Utilized for standard journal entry testing procedures Maximums for meals / hotels can be charged, however actual receipts do not match the maximum 52

53 Reference Material Fraud Detection Using Data Analysis Techniques to Detect Fraud By David G. Coderre David G. Coderre has over 18 yrs of experience in the informatics field in both the public and private sector. He is a recognized expert on CAATTs development and usage and has written numerous articles for leading international journals. He has also taught the use of CAATTS to auditors in various governments, including Canada, Indonesia and Mexico. Mr. Coderre holds an MBA from the University of Toronto and is a member of the Institute of Internal Auditors CAATTs & other BEASTs for Auditors Computer Assisted Audit Tools and Techniques (CAATTs) & Beneficial Electronic Audit Support Tools (BEASTs) By David G. Coderre 53

54 APPENDIX Common Automated Auditing Techniques Payables Employees Receivables & Sales Inventory & Purchasing Cash Fraud Vendors Fraud A/R & Sales Fraud - Purchasing 54

55 Payables Tab Area Audit Test Purpose of Test Data Extracts Required Payables & Disb. A/P Aging Verify Aging Test Accuracy of Client Aging Payables & Disb. A/P Activity Duplicate Payments Payables & Disb. Vendors Common vendor names Payables & Disb. Vendors Purchases fluctuation Identify Duplicate Payments Verify existence of vendors with common names Identify large fluctuations as compared to PY Invoice #, invoice date, invoice amount, due date Check# / wire #, amount, date, vendor/payee #, vendor invoice #, amount Vendor master data: name, address, phone, contact name Purchases by vendor PY and FY ACL function Age Classify/summariz e/sort Index Comparison sort Data Extract Source A/P subledger Check register / payment data A/P / Purchases master data Purchases / A/P payments 55

56 Employees Tab Area Audit Test Purpose of Test Data Extracts Required Employees Human Resources Payroll validity Identify duplicate addresses, ss#, bank accts Employees Human Resources Timely assoc. disposition Employees Human Resources Match payroll with employee master Verify exemployees no longer on payroll Verify only employees get paid HR data: employee name, ss#, address, chk acct#, accrued vacation, vacation taken, overtime billed, phone # List of terminated employees: employee name, ss#, address, chk acct#, accrued vacation, vacation taken, overtime billed, phone # HR data as above; Payroll run ACL function Duplicates search Join / match Data Extract Source Payroll records Payroll records; HR terminations Join / match Payroll records / HR master 56

57 Receivables & Sales Tab Area Audit Test Purpose of Test Data Extracts Required Receivable & Sales Receivable & Sales Receivable & Sales Receivable & Sales A/R Aging Verify Aging Test Accuracy of Client Aging A/R Aging Customer Balances Identify sig. cust. in AR aging; test accuracy of cust. balances per client; identify cust. with net credit balances A/R Sampling CMA Sampling Perform CMA sampling of receivables A/R Controls Customers who are employees Identify any customers who are employees Invoice #, invoice date, open invoice amount Customer #, customer name, item#, item date, open amount Customer #, customer name, item#, item date, open amount Customer #, customer name, item#, item date, open amount ACL function Age Classify/ summarize/ sort Sample Join / match / merge Data Extract Source A/R Subledger A/R Subledger A/R Subledger Customer A/R master data; HR master 57

58 Inventory & Purchasing Tab Area Audit Test Purpose of Test Data Extracts Required Invty & Purch Inventory Aging Verify Aging Test Accuracy of Client Aging Invty & Purch Invty & Purch Invty & Purch Invty & Purch Invty & Purch Inventory Subledger Inventory Subledger Inventory Subledger Inventory Sampling Purchasing Controls Negative / $0 Cost Items Negative Qty Items Duplicate Sku's, Costs CMA Sampling Purchases under review limit Identify items with negative or zero cost Identify items with negative qty Identify Duplicate Sku #, description or cost Perform CMA sampling of inventory Identify purchases just under the $ limit that requires more approval sku #, description, qty, unit cost, last purch date, last sale date sku #, description, qty, unit cost sku #, description, qty, unit cost sku #, description, qty, unit cost sku #, description, qty, unit cost ACL function Age Sort Sort Sort/sequence Sample Data Extract Source Inventory subledger Inventory subledger Inventory subledger Inventory subledger Inventory subledger Purchases detail Index Purchases g/l 58

59 Cash Tab Area Audit Test Purpose of Test Data Extracts Required Cash Bank Reconciliations Cutoff Identify reconciling items after reconciliation cutoff date Reconciling items: date, item#, description, amount ACL function Sort, filter Data Extract Source Reconciling items detail 59

60 Fraud - Vendors Tab Area Audit Test Purpose of Test Data Extracts Required Fraud Vendors Vendors who are employees Identify any vendors that are employees Fraud Vendors Check sequence Identify any checks out of sequence Fraud Vendors Duplicate payments Identify any duplicate payments Fraud Vendors Duplicate POs Extract invoices posted with duplicate PO's Vendor master and HR master ACL function Join / match / merge FY A/P Payments Sequence analysis Data Extract Source Vendor and HR Masters A/P check runs FY A/P Payments Extract A/P check runs FY A/P Payments with invoice #, PO #, amount, payee, check# Extract; statistics A/P check runs 60

61 Fraud A/R & Sales Tab Area Audit Test Purpose of Test Data Extracts Required Fraud A/R Employee associate purchases Review associate purchases for large amounts Fraud A/R Write-offs Determine validity of write-offs Fraud A/R Customers with no contact phone number Identify any customers with no phone number FY Employee purchases; name, date, amount, item, qty, pymt history FY A/R system write-offs; legal Fraud A/R / Sales Sales / A/R match Identify all A/R customer customers in sales data; Sales master that are not in A/R data Fraud A/R Duplicate Identify any A/R customer data accounts within duplicates in A/R / Sales master the portfolio portfolio data Fraud Sales/A/R Refunds Identify high volume refund customers ACL function Statistics, index Data Extract Source Employee purchases detail Statistics, index All FY write-offs for specific A/R w/offs accounts data A/R customer data Index, filter A/R master data Customer name; refund amount, date Merge / join Extract Statistics A/R and Sales master A/R and Sales master A/R transaction data 61

62 Fraud - Purchasing Tab Area Audit Test Purpose of Test Data Extracts Required Fraud Purchasing Purchases under review limit Identify purchases just under the $ limit that requires additional approval ACL function Data Extract Source Purchases detail Index Purchases g/l 62

63 80 City Square Boston, MA P F AN INDEPENDENT MEMBER OF BAKER TILLY INTERNATIONAL 505 AFFILIATE OFFICES WORLDWIDE