Computer-aided Fraud Prevention and Detection: A Step By Step Guide with CD-ROM
David Coderre has over twenty years of experience in internal audit, management consulting, policy development, manage-ment information systems, system development, and application implementation areas. He is currently President of CAATS (Computer-Assisted Analysis Techniques and Solutions). He is the author of three highly regarded books on using data analysis for audit and fraud detection.
Table of Contents
CHAPTER 1: What Is Fraud?
Fraud: A Definition.
Why Fraud Happens.
Who Is Responsible for Fraud Detection?
What Is a Fraud Awareness Program?
Screening Job Applicants.
What Is a Corporate Fraud Policy?
CHAPTER 2: Fraud Prevention and Detection.
Determining the Exposure to Fraud.
Assessing the Risk that Fraud Is Occurring (or Will Occur).
Identifying Areas of High Risk for Fraud.
Looking at the Exposures from the Fraudster’s Perspective.
Approach 1: Control Weaknesses.
Who Could Benefit from the Identified Control Weaknesses?
What Can They Influence, Control, or Affect?
Can They Act Alone or Is Collusion Required?
Approach 2: Key Fields.
Which Data Fields Can Be Manipulated and by Whom?
Additional Fraud Risk Considerations.
Understanding the Symptoms of Fraud.
Being Alert to the Symptoms of Fraud.
Building Programs to Look for Symptoms.
Investigating and Reporting Instances of Fraud.
Implementing Controls for Fraud Prevention.
CHAPTER 3: Why Use Data Analysis to Detect Fraud?
Increased Reliance on Computers.
Developing CAATTs Capabilities.
Integrated Analysis and Value-Added Audit.
Recognizing Opportunities for CAATTs.
Developing a Fraud Investigation Plan.
CHAPTER 4: Solving the Data Problem.
Setting Audit Objectives.
Defining the Information Requirements.
Data File Attributes and Structures.
Assessing Data Integrity.
Overview of the Application System.
Overview of the Data.
CHAPTER 5: Understanding the Data.
Cross Tabulation/Pivot Tables.
Assessing the Completeness of the Data.
Filter or Display Criteria.
Sorting and Indexing.
CHAPTER 6: Overview of the Data.
Cross Tabulation/Pivot Tables.
CHAPTER 7: Working with the Data.
CHAPTER 8: Analyzing Trends in the Data.
CHAPTER 9: Known Symptoms of Fraud.
Known and Unknown Symptoms.
Fraud in the Payroll Area.
Fraud in the Purchasing Area.
Vendor Action and Employee Inaction.
Collusion between Vendor and Employee.
Symptoms of Purchasing Fraud.
Goods Not Received.
CHAPTER 10: Unknown Symptoms of Fraud.
(Using Digital Analysis).
Frequently Used Values.
Even Amounts and Rounding.
Least/Most Used Categories.
One Business Area/Another.
CHAPTER 11: Automating the Detection Process.
Fraud Applications or Templates.
Fraud Application Development.
CHAPTER 12: Verifying the Results.
Judgmental or Directed Sampling.
Quality Assurance Methodology.
Data Analysis and Prosecuting Fraud.
APPENDIX 1: Fraud Investigation Plans.
Insurance Policies—Too Good to Be True.
Paid by the Numbers.
APPENDIX 2: Application of CAATTs by Functional Area.
Accounts Receivable Tests.
Accounts Payable Tests.
General Ledger Tests.
Materials Management and Inventory Control Tests.
Salary and Payroll Tests.
Purchase Order Management Tests.
Policy and Administration Tests.
Vendor Management Tests.
Retail Loss Prevention Tests.
Sales Analysis Tests.
Work in Progress Tests.
Cash Disbursement Tests.
Customer Service Management Tests.
Real Estate Loans.
Credit Card Management.
Life Insurance Tests.
APPENDIX 3: ACL Installation Process.
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