Data Mining
Instructor: Scott Langlinais
Using Data Mining to Detect Fraud & Error
Today's technology means problems can remain hidden from auditors and managers. This has made the traditional audit techniques of small random or statistical samples less effective in detecting fraud and error. Data mining techniques allow the auditor and finance professional to quickly analyze those numerous transactions to seek odd patterns in the data which can be targeted for further testing.
In this one-day seminar, learn how to identify and expose the most common cost and revenue leaks and fraud schemes that afflict most organizations.
Participants will learn:
- What techniques have been used to successfully detect fraud and recover millions of dollars
- Why data mining techniques, when combined with traditional audit techniques such as observation and confirmation, result in the most powerful, most effective audits
- How to incorporate data mining techniques into audit programs to detect the symptoms of fraud and error
- Why data mining techniques increase the efficiency and effectiveness of regular audits
- What symptoms of fraud look like in data mined results
- How to avoid common pitfalls in data mining
- How to handle the common objections related to data mining
- How to apply data mining to audits of several different financial statement and operational areas
- How to apply data mining to the five-step approach to fraud detection
- How to improve your sampling techniques with data mining
- Why data mining allows you to test one hundred percent of a population
The Basics of Data Mining
- What is data mining?
- Basic fraud concepts
- Ten items to include in your fraud policy
- Data mining and the five-step approach to fraud detection
- Barriers to effective data mining
- What can go wrong
Basic Data Mining Techniques
- Check control totals
- Sort fields
- Review field statistics
- Summarize key data fields
- Simple extractions
- Search for key phrases within the data
- Relating exposures and symptoms to audit program steps
Intermediate Data Mining Techniques
- Field manipulation and searching for duplicate entries
- Stratify fields to detect spikes around key numbers and dates
- Benford's Law
- Join databases with related data
- Relating exposures and symptoms to audit program steps
Advanced Data Mining Techniques
- Extracting portions of data fields
- Conditionals
- Creating your own databases to support investigations
- Continuous monitoring
- Relating exposures and symptoms to audit programs steps
Data Mining Testing Programs
- Accounts Payable
- Accounts Receivable
- Payroll
- Procurement
- Revenues and Receivables
- Travel Expense