1. Ed Tobias, CISA, CIA
June 8, 2010
Data Mining

2. Topics
Introduction & Current Perceptions
What is Data Mining?
How is Data Mining used?
Why is Data Mining important?
Questions
3. A little story involving
New G/L system
Curious Audit Manager
Questionable accounting entries
4. Introduction
IT Audit Manager for Hillsborough County
Certified as a CISA and CIA
Spend 50% doing Data Mining
Audit Risk Assessment
Testing control effectiveness
Compliance
Fraud Detection
5. Introduction
Who are you?
Accountants
Auditors
Consultants
Other industries
6. Current Perceptions about DM
What do you think Data Mining is?
7. Heard of CAATs?
Computer Assisted Audit Techniques
Formerly a specialized skill for IT Auditors
Common in every audit
Term is practically obsolete
8. What is Data Mining?
Automate the detection of relevant patterns
Look at current & historical data
Predict future trends
Efficient method for analyzing large amounts of data
Enhance key item sampling
Means for continuous auditing
9. How is Data Mining used?
Proactively review business processes
Identify anomalies
Risk Assessment
Reactively assist law enforcement in investigations
10. How is Data Mining used?
Outside of Audit, DM is used to generate revenue
Automating the detection of relevant patterns
Look at current & historical data
Predict future trends Predictive Analysis
aka Business Intelligence / Data Warehouse
11. Charity Fundraising
May, 2010 issue of SmartMoneymagazine
2 data mining articles
12. Charity Fundraising
Hospital in San Diego, CA
Patients get their treatment and their assets scanned
Donor research on your salary history, LinkedIn connections, satellite images of your pool
13. Charity Fundraising
Brown University 6 of 10 donors who gave $100K+ identified through DM
14. Charity Fundraising
ASPCA using DM and Predictive Analysis to determine donors
4x donations over 5 years - $80M
15. Charity Fundraising
Charitys Junk Mail Strategy
Mailings
Double Dip another request
Virus effect
Dear Friend personal recognition
16. How is Data Mining used?
Audit Process
Risk Assessment
Control Assessment
Observations
Fraud Detection and Prevention
17. How is Data Mining used?
Risk Assessment
Data analysis for high risk areas
High Dollar amounts
Potential for fraud
Potential for non-compliance
18. How is Data Mining used?
Risk Assessment
What can be detected?
Potential fraud or control weaknesses
Duplicate vendors
Duplicate invoices
Duplicate amounts
Benfords Law identify suspicious transactions
Focus audit on high risk areas
19. How is Data Mining used?
Control Assessment
Traditional audit used sampling approach
Auditors placed disclaimers regarding the accuracy of their statistical sampling
Not affordable or available anymore
Total assurance & clear indication of errors
DM uses 100% of transactions
Increases credibility & value of audit
20. Why is Data Mining important?
Examples of Data Mining
Proactive - Purchasing and Procurement
Reactive - Health Plan Auditing
21. Purchasing and Procurement
Common area for fraud
Abuse of financial authority
Technical manipulation of specifications
Internal collusion to circumvent controls
External collusion with suppliers
Manipulation of bid review
Overbilling
Bogus invoices
22. Purchasing and Procurement
2003 review - five local government procurement agencies in London
Purchasing managers
Specified the bid criteria
Suggested pre-approved companies
Suspected in collusion with suppliers
DM used to identify possible trends
23. Purchasing and Procurement
Analyzed win/lose statistics of the vendors
Number of bids won
Number of bids lost due to cost
Number of bids where vendor failed approval
Number of bids lost for other reasons
24. Purchasing and Procurement
Determined two key elements:
Vendors that consistently lost their bids shadow bidders
Vendors that won over 95% of bids
Team of forensic experts were used for contract review and work performed
25. Purchasing and Procurement
Collusion to circumvent controls in place
Required to have five bids
Used shadow bidders
Bid review group
Ensured that a selected (and corrupt) supplier was chosen every time
Allowed substitution of inferior materials
26. Health Plan Auditing
2010 case study Conducted by St. Josephs Univ. & Healthcare Data Mgmt
Two large companies health insurance claims data over two year period
Company A 108,000 claims, $25.3M paid
Company B 464,000 claims, $118.4M paid
27. Health Plan Auditing
Compared the results of 100% auditing vs. random-sampling claims (300-400 samples)
100% auditing produced very distinct results
Company A - $3.12M exception claims
Company B - $5.47M exception claims
28. Health Plan Auditing
1 Average of the two Post-Audit years
29. Health Plan Auditing
30. Health Plan Auditing
Company A - $3.12M in exception claims (9,315 records)
31. Health Plan Auditing
Company B - $5.47M in exception claims (20,395 records)
32. Health Plan Auditing
Random-sampling claims- used best analysis to simulate the audits
Using exceptions from the 100% auditing approach
100 random samples of 300 exceptions
100 random samples of 400 exceptions
Statistically close to their population of exceptions parameters from 100% auditing
33. Health Plan Auditing 300 samples

On average, random-sampling missed from $2.91M - $5.39M of exception claims paid