Why Should You Attend:
Payroll fraud, one of the most widespread types of employee theft of all the accounting fraud scams is the toughest to detect. The different kinds of prevailing scams make it more difficult to stop payroll fraud. What can you do to implement policies, procedures, and strategies to find out, eliminate and thwart payroll fraud? We welcome you to attend this 90-minute webinar to discover:
Signs to watch out for when identifying payroll fraud
Procedures you can implement to prevent fraud
How to use audits to identify patterns and red flags and stop scams
The most common risk factors companies face in dealing with payroll fraud
Real examples: Strategies drawn from actual fraud cases
Areas Covered in the Webinar:
Recognizing Payroll Fraud: Different kinds of Scams & Warning Signs to be aware of
The tell-tale signs of fraud: What to watch out for in employee behavior
Recognize your risk factors: Current issues companies are facing & how to prepare
The typical suspects: Common scams, schemes & plots for payroll fraud
Strategies to Detect & Prevent Fraud with Internal Controls & Policies
Guidelines on how to design red flag alerts into your payroll accounting
Expert tips on how to build internal controls to prevent fraud
Payroll scam case studies & their outcomes: Key take-home lessons
How to Use Payroll Audits to Secure your Organization Against Fraud & Negligence
Audit frequency: What should be the frequency of audits to be effective?
Tips on the various kinds of self-audits & approaches to conducting them
Common problems auditors face when it comes to fraud & how to avoid them
Payroll fraud, one of the most widespread types of employee theft of all the accounting fraud scams is the toughest to detect. The different kinds of prevailing scams make it more difficult to stop payroll fraud. What can you do to implement policies, procedures, and strategies to find out, eliminate and thwart payroll fraud? We welcome you to attend this 90-minute webinar to discover:
Signs to watch out for when identifying payroll fraud
Procedures you can implement to prevent fraud
How to use audits to identify patterns and red flags and stop scams
The most common risk factors companies face in dealing with payroll fraud
Real examples: Strategies drawn from actual fraud cases
Areas Covered in the Webinar:
Recognizing Payroll Fraud: Different kinds of Scams & Warning Signs to be aware of
The tell-tale signs of fraud: What to watch out for in employee behavior
Recognize your risk factors: Current issues companies are facing & how to prepare
The typical suspects: Common scams, schemes & plots for payroll fraud
Strategies to Detect & Prevent Fraud with Internal Controls & Policies
Guidelines on how to design red flag alerts into your payroll accounting
Expert tips on how to build internal controls to prevent fraud
Payroll scam case studies & their outcomes: Key take-home lessons
How to Use Payroll Audits to Secure your Organization Against Fraud & Negligence
Audit frequency: What should be the frequency of audits to be effective?
Tips on the various kinds of self-audits & approaches to conducting them
Common problems auditors face when it comes to fraud & how to avoid them
Speakers
Peter Goldmann is president of FraudAware LLC, the publisher of White-Collar Crime Fighter, a widely read online information source for internal auditors, controllers, corporate counsel, financial operations managers and fraud investigators.Peter is the author of Fraud in the Markets: Why it Happens and How to Fight It, published by John Wiley & Sons, as well as four other anti-fraud books.
Peter also developed FraudAware, the leading employee fraud awareness training program. A user-friendly, customized learning tool, FraudAware uses workshop, Webinar and E-Learning formats to educate employees and managers at all levels in how to detect, prevent and report incidents of fraud or suspicious conduct.
Peter holds a Bachelor’s degree from the University of Michigan and a Master’s from the London School of Economics
He can be reached at pgoldmann@fraudaware.com
