Scott Langlinais, CPA

Scott has over 20 years of experience in fraud detection and investigation. He has planned and conducted examinations related to executive corruption, bribes, kickbacks, vendor overbilling and underperformance, and employee theft. Scott regularly reports his findings to Boards of Directors, has testified in court on fraud matters, and has submitted his findings to federal law enforcement.

Scott regularly employs data analysis software to develop continuous monitoring programs and to perform searches for circumstances such as: unbilled revenues, unauthorized cash transfers, employee reimbursement and P-card improprieties, and payments to fictitious vendors and employees. He develops strategic plans including risk analyses, policies & charters, continuous monitoring, Sarbanes-Oxley (SOX) compliance, and procedures for effective internal consulting for organizations of all sizes across various industries.

He has authored white papers on fraud prevention, detection, and response for the International Risk Management Institute and serves as a regular guest speaker at nationwide conferences and chapter meetings held by the IIA, the ACFE, the AICPA, and ISACA. Prior to starting his own practice in 2003, Mr. Langlinais held public accounting and internal audit leadership positions, and served as Chief Audit Executive for a NASDAQ 100 software company. He received a BBA degree from the University of Notre Dame in 1991.

• 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.

• 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.

• 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.

• 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.