Financial Fraud Investigations - Example

FОRЕNSIС INVЕSТIGАТIОN – Соnquеst Bikеs Unit code and Name Assessment Task Student’s Name Student ID Date of Submission Lecturer’s Name WC 1613 EXECUTIVE SUMMARY This report is prepared by the Financial Fraud Investigations Firm for the management of Conquest Bikes at the request of its management. This report is compiled after a deep analysis and critical evaluation of company’s records using the SAS Enterprise Guide. Based on FFI’s investigation of product, order, payment, and employee records, various issues and their possible causes were uncovered as affecting the company the period after receivership. These are: Weak internal control systems of databases leading to errors such as missing product and invoice IDs, or unidentified data that can lead to inappropriate decision-making. Weak internal control of stock management leading to mismatch of stock-take figures and QOH. Weak internal control of payment activities leading to fraudulent disbursements of checks and invoices. Inadequate auditing procedures leading to the creation of fictitious vendors as detected by the address of an employee (ID 11023) matching with that of a vendor. Recommendations to amend the problems in the company and help prevent further loss and promote effective decision-making in the future are: Generally, adopt an information technology strategy to support business objectives, and monitor risks through IT governance. In regards to specific problems, the company should: Enhance technology use in detecting suspicious activities that can lead to stock theft and take active participation in physical stock counting. Establish effective and efficient internal controls to prevent inventory manipulation. Assign the inventory recording to an employee who does not participate in physical stock counting. Duties should be segregated to eliminate the chances of accounts manipulation by a fraudulent person. Intensify auditing procedures in the organization and have both internal and external auditors search actively for fraud to prevent asset misappropriation. Traditional audit procedures can be combined with modern software such as Microsoft Access to detect anomalies that can be missed with either method. Conduct background checks on employees before they begin working for the company. Hire a competent HR team to select employees with desirable qualities such as integrity. TABLE OF CONTENTS EXECUTIVE SUMMARY 2 1.Introduction 5 2.Fraud situation in Australia 5 3.Findings and Results 6 3.1Anomalies in product dataset 6 3.1.1. QOH and stock take mismatch 6 3.1.2 Missing product ID 7 3.2Anomalies in order detail dataset 7 3.2.1 Missing Invoices 7 3.2.3 Different amount between actual cost and amount with order no. 8 3.2.4 Mismatch between actual amount and invoice amount 8 3.3Anomalies in payments dataset 9 3.3.1 Duplicate payment for some orders 9 3.3.2 Mismatch in invoice and payment amounts 10 3.4Anomalies in employee dataset 10 3.4.1 Similar address of employee and vendor 10 3.4.2 Frequency of anomalies among employees 11 4.Discussion 11 a.Discussion on stock anomalies 11 b.Discussion on order-detail anomalies 12 c.Discussion on payment anomalies 12 d.Discussion on employee data set anomalies 13 This section discusses the anomalies discovered in the employee data set. 13 5.Recommendations 14 6.Conclusion 16 References 16 Appendix 17 a.Figure showing difference between QOH and stock take. 17 b.Actual cost differs with invoice amount and order number 18 18 c.Product category table 18 d.List of employees 19 e.List of vendors 23 f.Cressey Fraud Triangle 28 1. Introduction Conquest Bikes is an Australian firm that deals bikes and related accessories targeted for adventure enthusiasts. The company has experienced critical internal problems resulting from internal control and fraudulent activities. The objective of this report is to analyse and avail information with regards to the issues the company has experienced, its causes, and way forward. The report is divided into four major sections: 1) Background discussion of the fraud situation in Australia 2) Findings with regards to fraud issues resulting from analysis of data for Conquest Bikes 3) Discussion of the fraud issues at Conquest Bikes 4) Recommendations that Gary Maine can apply to his company. 2. Fraud situation in Australia According to the Australian Institute of Criminology, the prevalence of fraud within the Australian economy is rising every year and costs the economy at least $8.5 billion per year. Fraud has a devastating impact on companies and the economy at large as it leads to massive financial loss (Levi & Smith, 2011). Reasons contributing to increased cases of fraud in Australia include inadequate preparation measures by Australian companies to detect and prevent fraud against their businesses with many lacking progress in developing, and or implementing fraud control strategies. Information technology, increased educational standards of perpetrators, and pace of business are some of the risk factors to identity theft leading to fraud within the Australian economy (Choo, Smith, & McCusker, 2007). It is important to install and implement fraud prevention/detection systems in order to save the organization and economy from fraud-related losses (Yuka & Smith, 2003) 3. Findings and Results These sections discusses the major fraud issues going on at Conquest Bikers as revealed with analysis of data using the SAS Enterprise Guide and the Excel tools. 3.1 Anomalies in product dataset This section shows the anomalies discovered after analysis of the product dataset. 3.1.1. QOH and stock take mismatch There is mismatch between product quantity-on-hand (QOH) and stock take figures for seven products coded (See Table 1). The meaning of such anomalies and the implications they have for the company are discussed in section 4(a). Table 1: Differences in stock take and QOH Product ID Vendor ID Subcategory Product Description Color Cost List Price QOH Stock take Difference 299 AW00029454 12 HL Mountain Frame - Black, 44 Black $699.09 $1,349.60 9 2 7 312 AW00029424 2 Road-150 Red, 48 Red $2,171.29 $3,578.27 17 4 13 324 AW00029422 2 Road-650 Red, 62 Red $413.15 $699.10 9 0 9 361 AW00029443 1 Mountain-200 Black, 42 Black $1,251.98 $2,294.99 9 1 8 399 AW00029432 4 ML Mountain Handlebars NA $24.99 $56.29 11 0 11 561 AW00029467 3 Touring-1000 Yellow, 46 Yellow $1,481.94 $2,384.07 22 2 20 563 AW00029482 3 Touring-1000 Yellow, 54 Yellow $1,481.94 $2,384.07 9 0 9 3.1.2 Missing product ID The analysis of the product dataset revealed that there are four records of missing product IDs (see table 2) that cannot be accounted for. The implication that this kind of problem could have for the company is explained in Section 4(a). Table 2: Missing products ID in the product dataset Order No Product ID Amount EmployeeID InvoiceNo VendorID InvAmount ON100417   $64,959.72 11013       ON100419   $66,554.71 11013       ON100423   $21,009.19 11013       ON100430   $4,721.08 11013       3.2 Anomalies in order detail dataset This section shows the anomalies discovered after analysis of the order detail dataset. 3.2.1 Missing Invoices Five products had their payment ID and invoice amount recorded but had missing invoice numbers (See table 3). The implication that this anomaly could have to the company is indicated in section 4 (b). Table 3: Missing invoices in the order detail dataset InvoiceNo VendorID InvAmount PaymentID InvoiceNo1 InvAmount1 AuthorisedBy       PN001707 IN300590 $64,959.72 11023       PN001709 IN300592 $66,554.71 11023       PN001713 IN300596 $21,009.19 11023       PN001720 IN300603 $4,721.08 11023       PN001722 IN300605 $10,014.64 11023 3.2.3 Different amount between actual cost and amount with order no. For 12 products, the actual cost of products ordered does not match the amounts entered in the invoice and the amount paid for the products (see table 4). How this occurs and its implication for the company is discussed in section 4(b). Table 4: Different in amount between the actual cost and amount in order number Actual Cost Amount OrderNo ProductID EmployeeID Different_Amount $899.82 $1,129.30 ON100103 228 11023 -229.48 $7,580.76 $8,844.22 ON100104 238 11023 -1263.4598 $6,668.40 $8,002.08 ON100148 508 11023 -1333.68 $5,001.30 $5,668.14 ON100160 506 11023 -666.84 $379.90 $493.87 ON100172 469 11023 -113.97 $569.85 $683.82 ON100192 469 11023 -113.97 $9,280.00 $18,559.92 ON100232 355 11045 -9279.92 $271.20 $433.92 ON100243 518 11023 -162.72 $55,861.40 $100,191.56 ON100249 310 11023 -44330.16 $34,800.00 $69,599.70 ON100294 355 11035 -34799.7 $17,495.88 $18,953.87 ON100301 315 11023 -1457.99 $310.45 $496.72 ON100370 543 11023 -186.27 3.2.4 Mismatch between actual amount and invoice amount There is a mismatch between the actual amount and the amount stated in the invoice for 12 products (See table 5). How this occurs and its implication for the company is discussed in section 4(b). Table 5: Mismatch between actual amount and invoice amount in order detail dataset Actual Cost Amount EmployeeID InvoiceNo VendorID InvAmount $899.82 $1,129.30 11023 IN300276 AW00029467 $1,129.30 $7,580.76 $8,844.22 11023 IN300277 AW00029467 $8,844.22 $6,668.40 $8,002.08 11023 IN300321 AW00029432 $8,002.08 $5,001.30 $5,668.14 11023 IN300333 AW00029432 $5,668.14 $379.90 $493.87 11023 IN300345 AW00029432 $493.87 $569.85 $683.82 11023 IN300365 AW00029432 $683.82 $9,280.00 $18,559.92 11045 IN300405 AW00029425 $18,559.92 $271.20 $433.92 11023 IN300416 AW00029467 $433.92 $55,861.40 $100,191.56 11023 IN300422 AW00029476 $100,191.56 $34,800.00 $69,599.70 11035 IN300467 AW00029425 $69,599.70 $17,495.88 $18,953.87 11023 IN300474 AW00029432 $18,953.87 $310.45 $496.72 11023 IN300543 AW00029467 $496.72 3.3 Anomalies in payments dataset This section shows the anomalies discovered after analysis of the payments dataset. 3.3.1 Duplicate payment for some orders Duplicate payment was recorded for three set of orders (See table 6). Invoice number IN300306 was paid twice on the same date and authorized by two different employees while invoice numbers IN300434 and IN300573 had their payments authorised by the same employee each on subsequent days. Duplicate payment could be a genuine human error or a red flag for fraud. The implication that this anomaly has for the company is discussed in section 4(c). Table 6: Duplicate payments in the payment data set PaymentID PaymentDate InvoiceNo InvAmount ChequeNo AuthorisedBy PN001423 11-May-10 IN300306 $489.93 CN10239 11054 PN001423 11-May-10 IN300306 $489.93 CN10240 11023 PN001551 25-Jul-11 IN300434 $28,012.25     PN001551 26-Jul-11 IN300434 $28,012.25 CN10259 11023 PN001690 3-Sep-12 IN300573 $11,744.85     PN001690 4-Sep-12 IN300573 $11,744.85 CN10274 11023 3.3.2 Mismatch in invoice and payment amounts Based on the analysis, transactions for 14 products authorised by employee ID 11023 showed that the payment received was less than that indicated in the invoice (See table 6). The implication of this action for the company is discussed in section 4 (c). Table 6: Similar amount on order and invoice but different payment amount Amount EmployeeID InvoiceNo VendorID InvAmount PaymentID InvAmount1 AuthorisedBy $1,129.30 11023 IN300276 AW00029467 $1,129.30 PN001393 $899.82 11023 $2,024.80 11005 IN300295 AW00029451 $2,524.80 PN001412 $2,024.80   $1,199.76 11007 IN300299 AW00029451 $1,799.76 PN001416 $1,199.76   $2,112.40 11023 IN300316 AW00029446 $2,112.40 PN001433 $2,512.40   $19,439.82 11009 IN300332 AW00029451 $19,639.82 PN001449 $19,439.82   $635.80 11008 IN300400 AW00029451 $653.80 PN001517 $635.80   $11,879.78 11031 IN300403 AW00029451 $11,978.78 PN001520 $11,879.78   $1,923.56 11000 IN300404 AW00029451 $1,973.56 PN001521 $1,923.56   $33,126.55 11048 IN300413 AW00029451 $33,726.55 PN001530 $33,126.55   $19,630.55 11004 IN300427 AW00029451 $19,830.55 PN001544 $19,630.55   $7,357.53 11023 IN300431 AW00029434 $7,357.53 PN001548 $9,357.53   $13,089.38 11040 IN300526 AW00029451 $13,980.38 PN001643 $13,089.38   $26,178.75 11037 IN300530 AW00029451 $26,778.75 PN001647 $26,178.75   $1,133.86 11023 IN300600 AW00029459 $1,133.86 PN001717 $3,133.86   3.4 Anomalies in employee dataset This section shows the anomalies discovered after analysis of the employee dataset. 3.4.1 Similar address of employee and vendor Analysis of the employee dataset revealed that employee ID 11023 and vendor ID AW00029432 have the exact same address (see table 7). This kind of scheme and the consequences it has for the company is further addressed in the discussion section 4(d. i). Table 7: Employee and vendor with matching address Employee ID AddressLine1 Vendor ID Address EmployeeID1 Status 11023 30 Lake Nadine Place AW00029432 30 Lake Nadine Place 11023 Active 11023 30 Lake Nadine Place AW00029467 30 Lake Nadine Place 11023 Active 3.4.2 Frequency of anomalies among employees Based on data analysis on the employee database, employee ID 11023 has the highest frequency of anomalies, followed by employees ID 11013 and ID 11034 (See Table 5). The implications are discussed in detail in section 4(d ii). Table 5: High frequency of anomalies in the employee dataset EmployeeID COUNT PERCENT 11013 12 3.647416 11023 36 10.94225 11034 11 3.343465 4. Discussion This section discusses the possible reasons for the above anomalies to occur and the implications they have for the company. a. Discussion on stock anomalies Analysis of stock-take revealed that stock-take figures and product quantity-on-hand do not match. There is missing stock on the day of stock taking shown by the differences between stock take and QOH (see Chart 1). Information on missing product IDs is highly consistent with the involvement of employee ID 11013 and highest discrepancies are for the most expensive stock items. Stock figures anomalies and missing product information could be an indication of internal theft going on in the company, weak inventories incorrect input of data, or mistakes or deceit occurred during stock take. Chart 1: QOH and stock-take differences showing missing stock b. Discussion on order-detail anomalies Based on analysis of the five missing invoice numbers in the order-detail database, it is evident that employee ID 11023 was involved with their authorization. Missing invoices are a warning sign of fraudulent disbursements that are reflected as accounting irregularities. In cases where amount indicated on order is different from the actual cost amount, is an indication of wrong data input or fraudulent intentions where a fraudster will later retrieve the difference or try to make the account books to balance. Such problems are likely to occur when a company’s inventory controls are ineffective. Ineffective inventory controls and internal frauds bring high costs for the company and have adverse impact on the business’ bottom line where a lot of money is lost. c. Discussion on payment anomalies Duplicate payment anomaly reveals that the company lacks rigid standards to control how payments are made, for example, in the scenario employee ID 11054 and 11023 have both authorized the same payment on the same date. Usually, a legitimate vendor receives a duplicate payment but when notification is made to the employee in charge, a fraudulent employee may choose to intercept a refund which is pocketed rather than returned to the company. Employee 11023 is definitely enriching himself with ‘extra’ checks from the company to the customer through a billing scheme fraud. This is detrimental to the financial health of the company. Mismatch in invoice and amount paid could be an indication of over payment or less payment which if the intention is fraud could be billing schemes in the sense that the customer can organize to take the difference from the vendor for personal use. d. Discussion on employee data set anomalies This section discusses the anomalies discovered in the employee data set. i. Similar address with vendor This is a red flag that points to an occupational fraud being committed. The employee involved (ID 11023) is likely to have set up a fictitious vendor to which the Conquest Bikes has been paying invoices for fictitious products purchases from the ‘vendor’. The perpetrator has used a billing scheme which causes the company to lose its annual revenues through asset misappropriation. ii. High frequency of anomalies The analysis on employee data set has revealed activities linked with billing schemes where money is syphoned from the company to the employees’ pocket. Conquest Bikes is facing employee embezzlement/occupational fraud which is mostly perpetrated by employees ID 11023, 11, 013, and 11,034 at frequencies of 61.02%, 20.34% and 18.64% respectively (See Chart 2). The willingness of employees to commit fraud can be described using the Creesey Fraud triangle (see Appendix F). Fraudulent employees satisfy the components of the Cressey Fraud Triangle (Singleton & Singleton, 2010) in the following ways: They saw an opportunity to commit fraud when the company went into receivership as this has a weakening impact on the internal control systems. They are driven by pressure of greed as they already have decent incomes. They satisfy rationalization where integrity and ethical principles are low. Chart 2: Pie chart showing employees with high anomaly frequency 5. Recommendations Generally, Conquest Bikes should adopt an IT (information technology) strategy to support business objectives, and monitor risks. IT governance in organizations is today viewed as a requirement for business planning, growth and risk management (Black & Smith, 2003). In regards to specific problems detected from the analysis, the company should: Enhance technology use in detecting suspicious activities that can lead to stock theft. These include installing CCTVs in areas where cash and other company items are stored. Also, introduce system spot checks within the business to cover various areas such as disbursements claims. Stock taking should be performed more often and on a regular basis, and also conduct physical counting when inventories are delivered by comparing to the purchase order Establish effective and efficient internal controls to prevent inventory manipulation. Assign the inventory recording to an employee who does not participate in physical stock counting. Duties should be segregated where accounting tasks and handling of the company’s cash and assets are kept separately. This will eliminate the chances of employee tallying and manipulating the accounts receivable for example sale slips and invoices to their individual advantage. Intensify auditing procedures in the organization and have both internal and external auditors search actively for fraud to prevent asset misappropriation. Traditional audit procedures can be combined with modern software such as Microsoft Access to detect anomalies that can be missed with either method. Conduct background checks on employees before they begin working for the company. Hire a competent HR team to select employees with desirable qualities such as integrity. 6. Conclusion Conquest Bikes is facing employee embezzlement/occupational fraud which is mostly perpetrated by employees and should apply the recommendations above to prevent further loses. References Black, M., & Smith, R., G. (2003). Electronic monitoring in the criminal justice system. Trends & issues in crime and criminal justice No. 254. Canberra: Australian Institute of Criminology. Choo, K-K R, Smith, R., G & McCusker, R. (2007). The Future of Technology-enabled Crime in Australia Trends and Issues in Crime and Criminal Justice, No. 341, Canberra: Australian Institute of Criminology. Levi, M., & Smith, R., G. (2011) Fraud vulnerabilities and the global financial crisis, Trends and Issues in Crime and Criminal Justice, No. 422. Canberra: Australian Institute of Criminology. Singleton, T., & Singleton, A. (2010). Fraud auditing and forensic accounting. London: John Wiley & Sons, p44. Yuka, S., & Smith, R. (2003) Identifying and responding to risks of serious fraud in Australia and New Zealand, Trends and Issues in Crime and Criminal Justice, No. 270. Canberra: Australian Institute of Criminology. Appendix a. Figure showing difference between QOH and stock take. b. Actual cost differs with invoice amount and order number c. Product category table 1 Bikes 2 Components 3 Clothing 4 Accessories d. List of employees EmployeeID FirstName LastName BirthDate Gender YearlyIncome Education AddressLine1 Phone HireDate EndDate Status 11000 Lauren Walker 18-Jan-68 F $100,000.00 Graduate Degree 85 Scott Street 0455510209 20-Oct-09   Active 11001 Sydney Bennett 09-May-68 F $90,000.00 Bachelors 11 Tank Drive 0455510210 06-Dec-09   Active 11002 Jimmy Moreno 21-Dec-83 M $45,000.00 Partial College 5 Mark Twain 0455510169 22-Dec-09   Active 11003 Jill Jimenez 11-Apr-87 F $45,000.00 Partial College 111 Rose Ann Ave 0455510172 11-Feb-10 01-Dec-12 Inactive 11004 Chase Reed 07-Dec-75 M $40,000.00 High School 27 Alexander Pl. 0455510202 17-Mar-10   Active 11005 Daniel Johnson 04-Aug-59 M $50,000.00 High School 5 Sunnyvale Avenue 0455510216 27-Apr-10   Active 11006 Jordan King 20-Sep-78 M $55,000.00 High School 76 Rose Dr. 0455510191 01-Jan-10 10-Jul-12 Inactive 11007 Shannon Wang 26-Jun-86 F $45,000.00 High School 30 Saddlehill Lane 0455510164 19-May-10   Active 11008 Denise Stone 11-Jun-81 F $49,000.00 High School 6 Bentley Street 0455510184 17-Nov-10   Active 11009 Michele Nath 03-Apr-53 F $55,000.00 Partial College 4 Janin Pl. 0455510187 02-Jan-11   Active 11010 Theresa Ramos 22-Aug-88 F $48,000.00 High School 6 Detroit Ave. 0455510175 24-Jan-11   Active 11011 Ethan Zhang 12-Oct-78 M $40,000.00 Partial College 6 Nicholas Drive 0455510197 06-Feb-11 31-Dec-12 Inactive 11012 Jennifer Russell 18-Dec-78 F $60,000.00 Partial College 31 Augustine Drive 0455510200 11-Mar-11   Active 11013 Christine Yuan 22-Mar-80 F $40,000.00 High School 6 Orange St 0455510179 28-Mar-09 01-Aug-12 Inactive 11014 Wyatt Hill 28-Apr-79 M $50,000.00 Partial College 96 Northridge Ct. 0455510203 04-Jun-11   Active 11015 Clarence Rai 09-Oct-89 M $45,000.00 Partial College 4 Rivewview 0455510167 11-Jun-11   Active 11016 Russell Xie 17-Sep-78 M $60,000.00 Partial College 4 Oxford Place 0455510215 15-Jun-11 01-Dec-12 Inactive 11017 Christy Zhu 15-Feb-68 F $70,000.00 Bachelors 18 Village Pl. 0455510162 01-Aug-10 09-Oct-12 Inactive 11018 Todd Gao 27-Feb-54 M $80,000.00 Bachelors 88 Geneva Ave 0455510189 25-Aug-11   Active 11019 Destiny Wilson 03-Sep-78 F $40,000.00 Partial College 81W. Lake Dr. 0455510201 03-Oct-09 02-Feb-12 Inactive 11020 Julio Ruiz 05-Aug-65 M $70,000.00 Bachelors 73 Humphrey Drive 0455510163 10-Oct-11   Active 11021 Chloe Young 27-Feb-79 F $50,000.00 Partial College 244 Willow Pass Road 0455510196 11-Oct-11   Active 11022 Jessie Zhao 07-Dec-90 M $45,000.00 Partial College 2 Valencia Place 0455510166 12-Oct-11   Active 11023 Carl Andersen 12-Oct-53 M $70,000.00 Graduate Degree 30 Lake Nadine Place 0455510186 21-Oct-09   Active 11024 Megan Sanchez 13-Jun-77 F $70,000.00 Partial College 17 Paraiso Ct. 0455510199 02-Nov-11   Active 11025 Deanna Munoz 10-Mar-52 F $50,000.00 Partial College 25 B Way 0455510178 09-Nov-11   Active 11026 Heidi Lopez 07-Aug-62 F $55,000.00 Partial College 4 Via Cordona 0455510183 13-Nov-11   Active 11027 Jessica Henderson 09-Oct-73 F $60,000.00 Partial College 3 Ironwood Way 0455510204 16-Nov-11   Active 11028 Amanda Carter 16-Oct-77 F $60,000.00 Partial College 58Escobar 0455510194 17-Nov-11   Active 11029 Janet Alvarez 06-Dec-65 F $70,000.00 Bachelors 26 Berry Dr 0455510171 14-Jan-10 19-Jul-12 Inactive 11030 Jaime Nath 23-Sep-69 M $56,000.00 High School 27 Rainbow Dr 0455510182 26-Dec-11   Active 11031 Ashlee Andersen 01-Apr-54 F $80,000.00 Partial College 5 Highland Road 0455510207 07-Jan-11 28-Nov-12 Inactive 11032 Nathan Simmons 24-Feb-76 M $60,000.00 Partial College 170 Shaw Rd 0455510214 10-Jan-12   Active 11033 Jaclyn Lu 27-Feb-70 F $40,000.00 High School 28 Mazatlan 0455510177 24-Jan-12   Active 11034 Seth Edwards 11-Oct-78 M $40,000.00 Partial College 9 Valley Crest 0455510206 26-Jan-10 01-Nov-12 Inactive 11035 Luke Lal 07-Mar-78 M $40,000.00 High School 32 Landing Dr 0455510205 30-Jan-12   Active 11036 Gilbert Raje 05-Mar-52 M $50,000.00 Partial College 1 The Trees Dr. 0455510174 01-Feb-12   Active 11037 Alan Zheng 07-Sep-91 M $40,000.00 High School 21 Gainborough Dr. 0455510185 06-Feb-12   Active 11038 Carol Rai 18-Jul-80 F $40,000.00 Partial High School 64 Madrid 0455510208 14-Feb-12   Active 11039 Jacquelyn Suarez 06-Feb-64 F $70,000.00 Bachelors 78 Corrinne Court 0455510165 17-Feb-12   Active 11040 Marc Diaz 27-Apr-54 M $80,000.00 Partial College 45 Sinaloa 0455510188 05-Mar-10 27-Dec-12 Inactive 11041 Noah Powell 02-Sep-75 M $55,000.00 High School 94 Marion Ct 0455510192 05-Mar-12   Active 11042 Shannon Carlson 01-Apr-64 M $70,000.00 Bachelors 9 Northgate Road 0455510173 16-Mar-12   Active 11043 Ian Jenkins 06-Aug-68 M $100,000.00 Bachelors 72 Hudson Ave. 0455510213 19-Jan-10 16-Aug-12 Inactive 11044 Jon Zhou 17-Mar-54 M $80,000.00 Partial College 74 Hemlock Ave. 0455510190 24-Mar-10 31-Dec-12 Inactive 11045 Ebony Gonzalez 19-Jun-79 F $55,000.00 High School 5 Condor Place 0455510176 02-Apr-12   Active 11046 Harold Sai 03-Apr-86 M $45,000.00 Partial College 11 Leeds Ct. 0455510170 06-Apr-12   Active 11047 Jeremy Powell 22-Nov-75 M $40,000.00 High School 180 Potomac Dr. 0455510181 15-Apr-11 01-Sep-12 Inactive 11048 Jon Yang 08-Apr-66 M $90,000.00 Graduate Degree 61 N. 14th St 0455510168 28-Apr-12   Active 11049 Ana Price 20-Aug-80 F $60,000.00 Partial College 166 Stonyhill Circle 0455510195 30-May-12   Active 11050 Leonard Nara 19-May-88 M $50,000.00 High School 66 La Vista Ave. 0455510212 11-May-12 06-Aug-12 Inactive 11051 Jesse Murphy 01-Aug-77 M $50,000.00 Partial College 30 Kingswood Circle 0455510211 22-Jun-12   Active 11052 Marc Martin 17-Dec-88 M $40,000.00 Partial College 53 Creekside Dr. 0455510180 24-Jan-11 12-Nov-12 Inactive 11053 Angela Murphy 07-Apr-75 F $55,000.00 High School 7 Virgil Street 0455510193 27-Jun-12   Active 11054 Chloe Garcia 27-Nov-77 F $40,000.00 Partial High School 5 Woodside Way 0455510198 26-Jul-12   Active e. List of vendors VendorID VendorContact Phone Address Suburb State PostCode Country AgreedPayType EmployeeID Status AW00029420 Linda Travers 40.32.2555 54, rue Royale Nantes   44000 France EFT 11036 Active AW00029421 Blake Williams 7025551838 8489 Strong St. Las Vegas NV 83030 USA EFT 11046 Active AW00029422 Ian Adams 03 9520 4555 636 St Kilda Road Melbourne Victoria 3004 Australia Cheque 11001 Active AW00029423 Tony Luo 40.67.8555 67, rue des Cinquante Otages Nantes   44000 France EFT 11049 Inactive AW00029424 Erik Gill 07-98 9555 Erling Skakkes gate 78 Stavern   4110 Norway EFT 11024 Active AW00029425 Tommy Xie 4155551450 5677 Strong St. San Rafael CA 97562 USA CC 11032 Active AW00029426 Neil Gomez (26) 642-7555 ul. Filtrowa 68 Warszawa   01-012 Poland EFT 11022 Closed AW00029427 Wesley Chen +49 69 66 90 2555 Lyonerstr. 34 Frankfurt   60528 Germany EFT 11037 Active AW00029428 Kristopher Perez 6505555787 5557 North Pendale Street San Francisco CA 94217 USA EFT 11021 Active AW00029429 Marie Suarez 2125557818 897 Long Airport Avenue NYC NY 10022 USA EFT 11025 Closed AW00029430 Donna Goel (91) 555 94 44 C/ Moralzarzal, 86 Madrid   28034 Spain EFT 11004 Active AW00029431 Marie Gutierrez 0921-12 3555 Berguvsvägen 8 Luleå   S-958 22 Sweden CC 11030 Active AW00029432 Gabrielle Roberts 31 12 3555 30 Lake Nadine Place Chatswood NSW 1734 Australia Cheque 11023 Active AW00029433 Thomas King 78.32.5555 2, rue du Commerce Lyon   69004 France EFT 11027 Inactive AW00029434 Donna Yuan +65 221 7555 Bronz Sok. Singapore   079903 Singapore EFT 11051 Active AW00029435 Stanley Rodriguez 2125557413 4092 Furth Circle NYC NY 10022 USA EFT 11023 Active AW00029436 Adriana Raman 2155551555 7586 Pompton St. Allentown PA 70267 USA EFT 11007 Inactive AW00029437 Haley Bell 6505556809 9408 Furth Circle Burlingame CA 94217 USA EFT 11012 Closed AW00029438 Terrance Fernandez +65 224 1555 106 Linden Road Sandown Singapore   069045 Singapore CC 11035 Active AW00029439 Laura Zhu +47 2267 3215 Brehmen St. 121 Bergen   N 5804 Norway EFT 11009 Active AW00029440 Rafael Cai 2035557845 149 Spinnaker Dr. New Haven CT 97823 USA EFT 11015 Active AW00029441 Adriana Patel (1) 356-5555 Estrada da saúde n. 58 Lisboa   1756 Portugal EFT 11026 Active AW00029442 Damien Yuan 20.16.1555 184, chaussée de Tournai Lille   59000 France EFT 11045 Inactive AW00029443 Gabriel Phillips (1) 42.34.2555 265, boulevard Charonne Paris   75012 France EFT 11002 Active AW00029444 Beth Blanco 6175555555 4658 Baden Av. Cambridge MA 51247 USA EFT 11005 Active AW00029445 Bonnie Lal 2035552570 25593 South Bay Ln. Bridgewater CT 97562 USA CC 11054 Closed AW00029446 Jamie Ma +81 06 6342 5555 1-6-20 Dojima Kita-ku Osaka 530-0003 Japan EFT 11028 Active AW00029447 Lindsay Deng 2125551500 2678 Kingston Rd. NYC NY 10022 USA EFT 11008 Active AW00029448 Lindsey Yuan 90-224 8555 Keskuskatu 45 Helsinki   21240 Finland EFT 11010 Inactive AW00029449 Laura Chen (171) 555-1555 Fauntleroy Circus Manchester   EC2 5NT UK EFT 11048 Active AW00029450 Bradley Chande +353 1862 1555 25 Maiden Lane Dublin   2 Ireland EFT 11038 Active AW00029451 Geoffrey Lopez 6175558428 16780 Pompton St. Brickhaven MA 58339 USA CC 11053 Active AW00029452 Meredith Romero (171) 555-2282 12, Berkeley Gardens Blvd Liverpool   WX1 6LT UK EFT 11020 Active AW00029453 Victor Vazquez (604) 555-3392 1900 Oak St. Vancouver BC V3F 2K1 Canada EFT 11039 Inactive AW00029454 Victoria Bradley 6175557555 7635 Spinnaker Dr. Brickhaven MA 58339 USA EFT 11000 Active AW00029455 Dawn Zeng 6265557265 78934 Hillside Dr. Pasadena CA 90003 USA EFT 11018 Active AW00029456 Lindsay She +612 9411 1555 Suntec Tower Three Singapore   038988 Singapore EFT 11042 Active AW00029457 Cynthia Kapoor 88.60.1555 24, place Kléber Strasbourg   67000 France EFT 11041 Inactive AW00029458 Danny Moreno +852 2251 1555 Bank of China Tower Central Hong Kong     Hong Kong EFT 11014 Active AW00029459 Marco Malhotra (93) 203 4555 Rambla de Cataluña, 23 Barcelona   08022 Spain EFT 11037 Active AW00029460 Andres Chander 3105552373 4097 Douglas Av. Glendale CA 92561 USA EFT 11021 Active AW00029461 Colin Xu 0372-555188 Taucherstraße 10 Cunewalde   01307 Germany EFT 11025 Inactive AW00029462 Clinton Hernandez 86 21 3555 Smagsloget 45 Århus   8200 Denmark EFT 11004 Active AW00029463 Lucas Gonzales (514) 555-8054 43 rue St. Laurent Montréal Québec H1J 1C3 Canada EFT 11030 Active AW00029464 Eugene Gao (91) 745 6555 Gran Vía, 1 Madrid   28001 Spain EFT 11033 Active AW00029465 Roy Gill 7605558146 361 Furth Circle San Diego CA 91217 USA EFT 11027 Closed AW00029466 Lance Jimenez (198) 555-8888 Garden House Cowes Isle of Wight PO31 7PJ UK Cheque 11051 Active AW00029467 Monica Mehta 61.77.6555 30 Lake Nadine Place Chatswood NSW 2067 Australia Cheque 11023 Active AW00029468 Jacqueline Morris 069-0555984 Magazinweg 7 Frankfurt   60528 Germany EFT 11007 Active AW00029469 Dominique Saunders 011-4988555 Via Monte Bianco 34 Torino   10100 Italy EFT 11012 Active AW00029470 Nathan Roberts +33 1 46 62 7555 27 rue du Colonel Pierre Avia Paris   75508 France EFT 11035 Closed AW00029471 Dana Ortega 30.59.8555 67, avenue de l'Europe Versailles   78000 France EFT 11009 Active AW00029472 Lacey Sharma 0221-5554327 Mehrheimerstr. 369 Köln   50739 Germany EFT 11015 Active AW00029473 Carmen Subram (604) 555-4555 23 Tsawassen Blvd. Tsawassen BC T2F 8M4 Canada EFT 11026 Active AW00029474 Jaime Raje 089-0877555 Berliner Platz 43 München   80805 Germany EFT 11045 Active AW00029475 Jared Ward 02 9936 8555 201 Miller Street North Sydney NSW 2060 Australia EFT 11002 Active AW00029476 Elizabeth Bradley 035-640555 Via Ludovico il Moro 22 Bergamo   24100 Italy EFT 11005 Active AW00029477 Neil Ruiz +61 2 9495 8555 Monitor Money Building Chatswood NSW 2067 Australia Cheque 11054 Active AW00029478 Darren Carlson 6175558555 39323 Spinnaker Dr. Cambridge MA 51247 USA EFT 11015 Active AW00029479 Tommy Tang +41 26 425 50 01 Rte des Arsenaux 41 Fribourg   1700 Switzerland EFT 11026 Closed AW00029480 Nina Raji 0897-034555 Grenzacherweg 237 Genève   1203 Switzerland EFT 11045 Active AW00029481 Ivan Suri +47 2212 1555 Drammensveien 126A Oslo   N 0106 Norway EFT 11002 Active AW00029482 Clayton Zhang +31 20 491 9555 Kingsfordweg 151 Amsterdam   1043 GR Netherlands CC 11005 Active AW00029483 Jésus Navarro 030-0074555 Obere Str. 57 Berlin   12209 Germany EFT 11054 Active f. Cressey Fraud Triangle Read More