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Severin Grabski Department of Accounting & Information Systems Michigan State University

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. The Good – Why Data Mining.

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Severin Grabski Department of Accounting & Information Systems Michigan State University

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  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

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

  12. The Missing • 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?

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

  16. 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! Questions

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

  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

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