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.