Computer Aided Fraud Prevention and Detection

Praise for Computer-Aided Fraud

Prevention and Detection: A Step-by-Step Guide

"A wonderful desktop reference for anyone trying to move from
traditional auditing to integrated auditing. The numerous case
studies make it easy to understand and provide a how-to for
those?seeking to implement automated tools including continuous
assurance. Whether you are just starting down the path or well on
your way, it is a valuable resource."

-Kate M. Head, CPA, CFE, CISA

Associate Director, Audit and Compliance

University of South Florida

"I have been fortunate enough to learn from Dave's work over the
last fifteen years, and this publication is no exception. Using his
twenty-plus years of experience, Dave walks through every aspect of
detecting fraud with a computer from the genesis of the act to the
mining of data for its traces and its ultimate detection. A
complete text that first explains how one prevents and detects
fraud regardless of technology and then shows how by automating
such procedures, the examiners' powers become superhuman."

-Richard B. Lanza, President, Cash Recovery Partners, LLC

"Computer-Aided Fraud Prevention and Detection: A Step-by-Step
Guide helps management and auditors answer T. S. Eliot's timeless
question, 'Where is the knowledge lost in information?' Data
analysis provides a means to mine the knowledge hidden in our
information. Dave Coderre has long been a leader in educating
auditors and others about Computer Assisted Audit Techniques. The
book combines practical approaches with unique data analysis case
examples that compel the readers to try the techniques
themselves."

-Courtenay Thompson Jr.

Consultant, Courtenay Thompson & Associates

Autorentext

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.

Zusammenfassung

Praise for Computer-Aided Fraud

Prevention and Detection: A Step-by-Step Guide

"A wonderful desktop reference for anyone trying to move from traditional auditing to integrated auditing. The numerous case studies make it easy to understand and provide a how-to for those?seeking to implement automated tools including continuous assurance. Whether you are just starting down the path or well on your way, it is a valuable resource."
-Kate M. Head, CPA, CFE, CISA

Associate Director, Audit and Compliance

University of South Florida

"I have been fortunate enough to learn from Dave's work over the last fifteen years, and this publication is no exception. Using his twenty-plus years of experience, Dave walks through every aspect of detecting fraud with a computer from the genesis of the act to the mining of data for its traces and its ultimate detection. A complete text that first explains how one prevents and detects fraud regardless of technology and then shows how by automating such procedures, the examiners' powers become superhuman."
-Richard B. Lanza, President, Cash Recovery Partners, LLC

"Computer-Aided Fraud Prevention and Detection: A Step-by-Step Guide helps management and auditors answer T. S. Eliot's timeless question, 'Where is the knowledge lost in information?' Data analysis provides a means to mine the knowledge hidden in our information. Dave Coderre has long been a leader in educating auditors and others about Computer Assisted Audit Techniques. The book combines practical approaches with unique data analysis case examples that compel the readers to try the techniques themselves."
-Courtenay Thompson Jr.

Consultant, Courtenay Thompson & Associates

Inhalt
Case Studies. Preface.

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?

Notes.

CHAPTER 2: Fraud Prevention and Detection.

Detecting Fraud.

Determining the Exposure to Fraud.

Assessing the Risk that Fraud Is Occurring (or Will Occur).

External Symptoms.

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.

Notes.

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.

Allegation.

Objective.

Audit Team.

Schedule.

Data Source.

Analysis.

Legal Authority.

Notes.

CHAPTER 4: Solving the Data Problem.

Setting Audit Objectives.

Defining the Information Requirements.

Accessing Data.

Data Paths.

Data File Attributes and Structures.

Assessing Data Integrity.

Overview of the Application System.

Overview of the Data.

Notes.

CHAPTER 5: Understanding the Data.

Computer Analysis.

Analysis Techniques.

Filter/Display Criteria.

Expressions/Equations.

Gaps.

Statistical Analysis.

Duplicates.

Sort/Index.

Summarization.

Stratification.

Cross Tabulation/Pivot Tables.

Aging.

Join/Relate.

Trend Analysis.

Regression Analysis.

Parallel Simulation.

Benford's Law.

Digital Analysis.

Confirmation Letters.

Sampling.

Combining Techniques.

Assessing the Completeness of the Data.

Filter or Display Criteria.

Expression/Equation.

Gaps.

Statistical Analysis.

Duplicates.

Sorting and Indexing.

Notes.

CHAPTER 6: Overview of the Data.

Summarization.

Stratification.

Cross Tabulation/Pivot Tables.

CHAPTER 7: Working with the Data.

Aging.

Join/Relation.

CHAPTER 8: Analyzing Trends in the Data.

Trend Analysis.

Regression Analysis.

Parallel Simulation.

Notes.

CHAPTER 9: Known Symptoms of Fraud.

Known and Unknown Symptoms.

Fraud in the Payroll Area.

Ghost Employees.

Terminated Employees.

Overpayment.

Fraud in the Purchasing Area.

Employee Activities.

Vendor Action and Employee Inaction.

Collusion between Vendor and Employee.

Symptoms of Purchasing Fraud.

Kickbacks.

Fixed Bidding. Goods Not Received.</...