Instructor: Scott Langlinais
Using Data Analysis to Detect Fraud & Error
Today's technology means problems can remain hidden from auditors and managers. Data analysis techniques, when combined with manual techniques, create the most effective and efficient audit tests for making cost recoveries, detecting errors, and uncovering symptoms of fraud.
In this one-day seminar, learn how to identify and expose the cost leaks and fraud schemes that afflict most organizations.
Participants will understand how to:
- Employ data analysis techniques to successfully detect fraud for cost recovery.
- Seamlessly merge data analysis techniques with traditional audit techniques such as observation and confirmation, to result in more effective audits.
- Incorporate data analysis techniques into audit programs to detect the symptoms of fraud and error in financial and operational areas.
- Identify symptoms of fraud in data mined results.
- Avoid common pitfalls in data analysis.
- Handle the common objections related to data analysis.
- Apply data analysis to the five-step approach to fraud detection.
- Use data analysis to improve your sampling techniques.
- Use data analysis to test all (or 100%) of a population instead of a sample.
The Basics of Data Analysis and Fraud Concepts
- What is data analysis?
- Data analysis and the five-step approach to fraud detection
- Common pitfalls in data analysis
- Improving sampling with data analysis
- What can go wrong - perpetrators and fraud acts
Applying Basic Data Analysis Techniques
to Fraud Detection
- Footing and using control totals to detect manipulation of reconciliation / adjustment spreadsheets
- Sorting data to highlight missing key data fields, stale transactions, or unusually large / small items
- Analyzing field statistics to detect unusual balances such as negative entries, posts in the middle of the night, and unusual weekend transactions
- Summarizing data to detect curious patterns such as unusually high spending on procurement cards, excessive travel, or gross budget overages
- Extracting round sum entries
- Searching for unusual descriptors within entries such as PLUG, ESTIMATE, CORRECT, REVERSE, ADJUST
- Seeking data fields that allow accountants to book one-sided entries
- Relating exposures and symptoms to audit program steps
Intermediate Data Analysis Techniques
- Field manipulation and searching for duplicate entries
- Date stratification to test transactions in accounts with unusual activity spikes at the beginning or end of a fiscal period
- Using numeric stratification to seek circumvention of approval authority
- Analysis using Benford's Law
- Joining databases to detect false vendors, ghosts on the payroll, and revenue loss
Advanced Data Analysis Techniques
- Extracting partial fields in the data to perform advanced testing such as highlighting payments sent to local post office boxes and detecting false social security numbers
- Using conditionals to create complex formulas for extractions
- Applying data analysis techniques to support investigations
- Assembling your testing into a continuous monitoring program