1. Discussion of: A Taxonomy to Guide Research on the Application of Data Mining to Fraud Detection in Financial Statement Analysis Severin Grabski Department of Accounting & Information Systems Michigan State University

2. The Good – Why Data Mining “Data mining outperforms rules-based systems for detecting fraud, even as fraudsters become more sophisticated in their tactics. “Models can be built to cross- reference data from a variety of sources, correlating nonobvious variables with known fraudulent traits to identify new patterns of fraud,”…” Source:http://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/data- mining-a-z-104937.pdf

3. The Good Builds upon Data Mining of E-Mail Research/Framework Liked Framework Incorporated Data Outside of the AIS into Data Mining (Fig. 5) Linked Data Mining to “Potential Payoff” Matrix (Fig. 6)

4. The Good Data Mining Makes the Most Sense When You Have a Story Need Institutional & Audit Knowledge Research Linked Fraud Types to a Story Account Schemes Evidence Schemes

5. The Missing Could not find a Precise Definition of “Data Mining” Is it “Big D” or “Little D”?

6. Knowledge Discovery in Databases - KDD Source:http://www.kmining.com/info_definitions.html

7. The Missing Data Mining Task Automatic (Semi-Automatic) Analysis of Large Quantities of Data to Extract Patterns, Anomalies, Dependencies

8. Data Mining Tasks Anomaly DetectionAssociation Rule Learning ClusteringClassification RegressionSummarization Sequential Pattern Matching

9. The Missing Data Mining Process Should be Based upon an Existing Standard Methodology CRISP-DM Cross Industry Standard Process for Data Mining

10. The Missing CRISP-DM Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment

11. CRISP-DM Source: http://en.wikipedia.org/wiki/File:CRISP-DM_Process_Diagram.png The Missing

12. List of Data Mining Techniques/Tools Suggestion of Appropriate Techniques to use in a Given Situation Example of Data Mining Tool Application

13. The Missing Title is “A Taxonomy to Guide Research on the Application of Data Mining to Fraud Detection in Financial Statement Analysis” Not Sure How the Taxonomy is Supposed to Guide Research

14. The Unanswered Where does Data Mining Most Benefit the Audit? Suspected Frauds? Entire Audit Process? PlanningRisk Assessment ExecutionTests of Controls ReportingSubstantive Tests

15. Questions Given the Benefits of Continuous Auditing, is Data Mining a “Temporary” Solution?

16. Questions Cost-Benefit of Data Mining w/r/t Potential Fraud Gao & Srivastava (2011) – 100 SEC Enforcement Actions 1997-2002 If 2800 NYSE & 3200 NASDAQ Firms Not Even.0028% Had Action!

17. Questions Cost-Benefit of Data Mining? Audit Firm Client Society (Investor)

18. Conclusion Liked Development of Framework Liked the Matrix (Fig. 6) Would Have Liked More: Precision Linkage to Data Mining Methodologies Linkage of Techniques to Audit Settings Use Outside of Fraud Audit