Data Analysis

Instructor:  Scott Langlinais

Detecting Fraud Using Data Analysis

Massive data sets within the organization's systems can hide symptoms of fraud from auditors and control personnel.  This tactical session delves into specific methods for auditors, investigators, and finance professionals to highlight symptoms of problems across all company processes.  Learn how to apply data analysis skills to effectively test one hundred percent of a transaction population and achieve a positive return-on-investment for your organization. 

This one-day workshop is not about what buttons to click; it is about strategies employed through the use of data analysis.  It is software-neutral.  The instructor will demonstrate techniques that can be handled by all of the programs.

Participants will understand how to:

  • Overcome mindsets that prevent us from properly addressing fraud.
  • Apply a consistent methodology for fraud detection.
  • Employ data analysis techniques used to successfully detect fraud.
  • Blend traditional methods of auditing with data analysis techniques.
  • Incorporate data analysis techniques into routine daily activities to improve detective controls.
  • Avoid common pitfalls related to data analysis.
  • Apply data analysis to audits of process areas common to all organizations.
  • Apply lessons from case studies to your own unique environment.
  • Use data analysis to test 100% of a population instead of a sample.

Preparing to Detect Fraud

  • Our role in fraud detection
  • Assessing Fraud Policy
  • A method for fraud detection
  • Data analysis in perspective
  • Perpetrators and fraud acts

Fundamental Data Analysis Techniques

  • Importing preferences
  • Using control totals to detect manipulation of reconciliations and spreadsheets
  • Sorting data to highlight key missing fields, stale transactions, odd dates, and unusually large / small items
  • Detecting anomalies through statistical analysis
  • Effective use of extractions, using logic operators to highlight odd transactions
  • How to spot patterns with summarizations and pivot tables
  • Application of fundamental techniques to case studies

Beyond Fundamentals

  • Searching for descriptors symptomatic of earnings management, fictitious payments and corruption
  • Effective uses of field manipulation
  • Duplicate key detection and exclusion
  • Date stratification to detect spikes in activity around a period end, symptomatic of earnings management
  • Numeric stratification and the circumvention of approval authority
  • Benford's Law and its application
  • Joining databases to detect false vendors, ghosts on the payroll, and revenue loss
  • Time block comparisons to detect escalating activity symptomatic of false vendors, cash misappropriation and "black hole" accounts


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