Better Business Through Data Analysis & Monitoring My Account
/Portals/0/NADevEventsImages/Industry Event_80.jpg

Detecting Fraud Using IDEA | Dallas, TX

Event Start Date: 9/26/2017 Event End Date: 9/27/2017

In this two-day hands-on seminar, attendees will apply data analysis techniques to real-life fraud scenarios using data files and case studies to identify and solve problems. Our goal is to teach audit and accounting professionals to detect fraud, waste and abuse early and address them professionally.  

Who should attend:  Auditors, Investigators, Accounting Professionals.  
Whether you are a seasoned investigator or someone seeking a better understanding of what can go wrong in your organization, you will benefit from this program.  Novice auditors or those new to data analysis will learn and employ fundamental detection techniques to real cases, while advanced users and investigators will apply their knowledge in new and challenging ways to detect buried symptoms within complex schemes.

Attendees should bring a laptop computer with IDEA software installed and updated.  The Instructor will provide downloadable data for use in class.  Don't use IDEA?  A free demo version of IDEA software will be provided to you.

From the opening exercise, we will employ a team approach to solving cases, as in the field.  Within IDEA, you will apply to actual case data: Creative Extractions; Statistical Analyses; Join-matches & mismatches; Trend Analysis; Pivot Tables; Embedded Functions; Field Manipulation; Numeric & Date Stratification; Benford's Law; Data Duplications & Exclusions; and more!

Scott Langlinais, CPA has dedicated over 20 years of his career to fraud detection and investigation. Business leaders across many industries have asked him to assess their environment and design strategies to help defend the organization's people, reputation and assets.

Mr. Langlinais employs IDEA data analysis software to sift through system transactions and seek indicators of fraud, waste and abuse.  Using such techniques, he has helped companies recover millions of dollars from vendor overpayments, corruption and unbilled revenues.

Prior to starting his own practice, Mr. Langlinais held accounting and internal audit leadership positions, most recently serving as Director of Internal Audit and Security for a NASDAQ 100 software company.  The International Risk Management Institute publishes his quarterly articles about fraud prevention.

Scott received a BBA degree from the University of Notre Dame. 

Attendees will understand how to:

  • Apply a five-step approach to fraud detection, and avoid the dangers of mishandling cases.
  • Blend traditional detection methods with data analysis techniques.
  • Correct problems with the data import, identifying missing and hidden data.
  • Keep data organized and use editable fields to isolate problems.
  • Employ more creative extractions to increase the chance of detecting wrongdoing.
  • Use effective techniques for detecting circumvention of system controls.
  • Extract symptoms embedded within blocks of data.
  • Identify symptoms of theft and fraudulent reporting within common processes.
  • Use principles of effective thinking and presentation to compile strong evidence.

Preparing to Detect Fraud

  • Factors that discourage us from detecting fraud.
  • Building discipline to handle wrongdoing and avoid the dangers of mishandling cases.
  • Applying a five-step approach to fraud detection.
  • Understanding what can go wrong and recognizing symptoms of wrongdoing.
  • Correcting problems with the data import, identifying missing and hidden data.
  • Keeping your data organized.

Detecting Fraud with IDEA

  • Identifying patterns through statistical review, data sorting, and field summarizations.
  • Effective and creative extractions for disbursements, employee reimbursements, liquid assets, revenues, and general ledger transactions.
  • Employing editable fields to flag anomalies.
  • Applying duplicate key detection/exclusion, and combining with field manipulation to detect circumvention of system controls.
  • Avoiding six common errors in data analysis.
  • Stratifying data to detect approval circumvention, earnings management, and money laundering.
  • Using special functions to carve out symptoms embedded within blocks of data.
  • Employing join-matches and mismatches to detect fictitious vendors, revenue leaks, and false shipments.
  • Extracting key-word symptoms of corruption and fraudulent financial reporting.
  • Application of pivot tables, Benford's law, and trend analysis.

Putting it All Together

  • Developing programs to detect theft and fraudulent reporting.
  • Applying techniques to solve cases from allegation to presentation of evidence.
  • Weaving techniques into a continuous monitoring program.
  • Using principles of effective thinking and presentation to compile effective evidence.

Event Location

  

Frequently Asked Questions for Training
Meet the IDEA Instructors
On-Site Training
Self Study Options
Training Course Descriptions

Hands down, IDEA is easier to use than other analytics tools. To fully maximize your use of IDEA and for faster implementation, we recommend new and experienced users take advantage of our many training opportunities. Our courses are offered throughout the U.S. and online for all levels of users (Beginner, Intermediate and Advanced). Many will even earn NASBA CPE credit and help you work toward CaseWare Analytics IDEA Certification.

The best thing about our training - our first-class instructors. We've been working with IDEA for over 20 years - we know the product inside and out. Our instructors are Certified Public Accountants (CPAs), Certified Fraud Examiners (CFEs) and Certified IDEA Data Analysts (CIDA) with experience working in the trenches of accounting, audit and finance. Plus, they make learning fun…now that’s worth signing up for!

Solutions Development Testimonial Quotation Marks

I learned A TON! I am very excited to start applying the new concepts I learned to projects at the office and to also share my knowledge with co-workers.