What You Will Learn

1. Introduction to Fraud Data Mining
 - What data mining can and can not do
 - Common data mining mistakes
 - Using the fraud scenario approach
 - Building the fraud data profile
 - Flowcharting the data mining decision tree analysis
 - Importance of correlation analysis

2. Data Mining Strategies
 - How fraud is concealed in the data
 - Searching the master file or the transactional data
 - Strategies for standard search routines

3. How to Build a Data Mining Plan
 - Eight steps to building a plan; the five data mining phases
 - Understanding the integrity of the data
 - Key data mining issues
 - Focus on fraud opportunity
 - Building the fraud data map

4. Data Analytics in Planning the Audit
 - Use of technology to create reports and work papers
 - Sampling strategies
 - Key work papers in data mining
 - Understanding the data
 - How to document the planning considerations

5. Data Mining for Shell Companies
 - Attributes of a shell company
 - Identifying the key data; how to build search routines
 - Interrogate master file data
 - Correlation to transactional data

6. Data Mining for Fraudulent Disbursements
 - False, pass thru, and over billing schemes
 - Duplicate and speed of payment schemes

7. Data Mining for Corruption
 - Attributes of facilitation payments
 - How to search to use word searching to locate suspicious payments
 - Hidden bribe payments or illegal gratuities
 - Locating conflict of interests

8. Data Mining Company Credit Cards
 - Abuse vs. fraud
 - Disguised personal expenses
 - Adverse publicity expenses
 - Locating hidden bribe payments

9. Data Mining for Payroll Fraud
 - Ghost employee and disguised compensation schemes
 - Over time fraud
 - Payroll adjustments schemes
 - Bonuses and commissions
 - Theft of payroll payments

10. Data Mining for Theft of Revenue and Cash Receipts
 - Searching for returns and adjustment schemes
 - Locating accounts receivable lapping schemes
 - Finding ghost or front customer schemes
 - Theft of customer credit schemes

11. Data Mining within the Financial Statements
 - Revenue recognition fraud
 - Journal entries testing for management override
 - Improper expenditure recognition
 - Falsifying accounts receivable aging
 - Budget manipulation