1. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Fraud Examination, 4E Chapter 6: Data-Driven Fraud Detection

2. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Learning Objectives  Describe the importance of data-driven fraud detection, including the difference between accounting anomalies and fraud.  Explain the steps in the data analysis process.  Be familiar with common data analysis packages.  Understand the principles of data access, including open database connectivity (ODBC), text import, and data warehousing.

3. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Learning Objectives  Perform basic data analysis procedures for fraud detection.  Read and analyze a Matasos matrix.  Understand how fraud is detected by analyzing financial statements.

4. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Data-Driven Fraud Detection  Using database queries and other methods to determine if those frauds may actually exist

5. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Anomalies Versus Fraud  Anomalies…  are not intentional  will be found throughout a data set  Fraud…  is intentional  is found in very few data sets  is like “finding a needle in a haystack”

6. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. The Data Analysis Process Proactive – Not Reactive  use reengineered methods to be effective  learn new methodologies, software tools, and analysis techniques  brainstorm the schemes and symptoms  a hypothesis-testing approach

7. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Proactive Method Of Fraud Detection The Six Steps of the Proactive Method: 1.Understand the business 2.Identify possible frauds that could exist 3.Catalog possible fraud symptoms 4.Use technology to gather data about symptoms 5.Analyze results 6.Investigate symptoms

8. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Understanding the Business Ways to understand a business:  Tour the business, department, or plant  Become familiar with competitor processes  Interview key personnel  Analyze accounting information  Review process documentation  Work with auditors and security personnel  Observe employees performing their duties

9. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Identify Possible Frauds  Divide the business into individual functions  Interview people in the business functions—ask questions like…  Who are the key players in the business?  What types of employees, vendors, or contractors are involved in business transactions?  How do insiders and outsiders interact with each other?  What types of fraud have occurred or been suspected in the past?

10. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Identify Possible Frauds  What types of fraud could be committed against the company or on behalf of the company?  How could employees or management acting alone commit fraud?  How could vendors or customers acting alone commit fraud?  How could vendors or customers working in collusion with employees commit fraud?

11. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Catalog Possible Fraud Symptoms Divided into five groups (Chapter 5):  Accounting anomalies  Internal control weaknesses  Analytical anomalies  Extravagant lifestyles  Unusual behaviors  Tips and complaints Example: Kickbacks

12. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Catalog Possible Fraud Symptoms  Red Flags of Kickbacks  Analytical Symptoms  Increasing prices  Larger order quantities  Increasing purchases from favored vendor  Decreasing purchases from other vendors  Decreasing quality

13. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Catalog Possible Fraud Symptoms  Red Flags of Kickbacks  Behavioral Symptoms  Buyer doesn’t relate well to other buyers and vendors  Buyer’s work habits change unexpectedly

14. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Catalog Possible Fraud Symptoms  Red Flags of Kickbacks  Lifestyle Symptoms  Buyer lives beyond known salary  Buyer purchases more expensive automobile  Buyer builds more expensive home

15. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Catalog Possible Fraud Symptoms  Red Flags of Kickbacks  Control Symptoms  All transactions with one buyer and one vendor  Use of unapproved vendors  Document Symptoms  1099s from vendor to buyer’s relative

16. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Catalog Possible Fraud Symptoms  Red Flags of Kickbacks  Tips and Complaints  Anonymous complaints about buyer or vendor  Unsuccessful vendor complaints  Quality complaints about purchased products

17. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Steps 4-6 4.Use technology to gather data about symptoms  Data analysis applications  Structured query language (SQL) 5.Analyze results  Screen results using computer algorithms  Real-time analysis and detection of fraud 6.Investigate symptoms  Pursue most promising indicators  Highlight frauds while they are still small

18. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Data Analysis Software ACL Audit Analytics  Powerful program for data analysis  Most widely used by auditors worldwide CaseWare’s IDEA  Recent versions include an increasing number of fraud techniques  ACL’s primary competitor Picalo  Similar to IDEA and ACL but incorporates “Detectlets”—small plug-ins to detect fraud indicators

19. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Data Analysis Software Microsoft Office + ActiveData  a plug-in for Microsoft Office  provides data analysis procedures  based in Excel and Access  less expensive alternative to ACL and IDEA SAS and SPSS  Statistical analysis programs with available fraud modules

20. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Data Access  Gathering the right data in the right format during the right time period. Methods include:  Open Database Connectivity (ODBC)  Text Import  Hosting a Data Warehouse

21. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Data Access Open Database Connectivity (ODBC)  standard method of querying data from corporate databases  a connector between analysis applications (ACL, IDEA, and Picalo) and the company databases (Oracle, SQL Server, and MySQL)  best way to retrieve data for analysis because  it can retrieve data in real time  it allows use of the SQL language  it allows repeated pulls for iterative analysis  it retrieves metadata (like column types and relationships) directly

22. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Data Access Text Import  Import data with a delimited text (CSV or TSV)  CSV: ID, Date, First Name, Last Name, Phone Number, etc. 342, 12/23/2007, Seth, Knab, 000-000-0000, etc.  TSV: IDDateFirst NameLast NamePhone 34212/23/2007SethKnab 000-000-0000  Import data with XML or other language

23. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Data Access Hosting a Data Warehouse  Data are imported, stored, and analyzed within ACL or other program  An all-in-one solution for the investigator

24. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Data Analysis  Analysis techniques that are most commonly used by fraud investigators:  Data Preparation  Benford’s Law  Digital Analysis  Outlier Investigation  Stratification and Summarization  Time Trend Analysis  Fuzzy Matching  Real-Time Analysis

25. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Data Analysis Matasos Matrix  One way to view the results of multiple indicators is to use a chart called a Matasos matrix:

26. Albrecht, Albrecht, Albrecht, Zimbelman © 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Financial Statement Analysis Approaches to Financial Statement Analysis: 1.comparing account balances from one period to the next 2.calculating key ratios and comparing them from period to period 3.performing vertical analysis 4.performing horizontal analysis