Download presentation
Presentation is loading. Please wait.
Published byMeghan Roberts Modified over 9 years ago
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
Similar presentations
© 2024 SlidePlayer.com. Inc.
All rights reserved.