Severin Grabski Department of Accounting & Information Systems Michigan State University
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Transcript of Severin Grabski Department of Accounting & Information Systems Michigan State University
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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
SystemsMichigan State University
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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
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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)
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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
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The Missing• Could not find a Precise Definition
of “Data Mining” • Is it “Big D” or “Little D”?
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Knowledge Discovery in Databases - KDD
Source:http://www.kmining.com/info_definitions.html
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The Missing• Data Mining Task
• Automatic (Semi-Automatic) Analysis of Large Quantities of Data to Extract Patterns, Anomalies, Dependencies
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Data Mining TasksAnomaly Detection Association Rule
Learning
Clustering Classification
Regression Summarization
Sequential Pattern Matching
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The Missing• Data Mining Process Should be
Based upon an Existing Standard Methodology
• CRISP-DM• Cross Industry Standard Process for
Data Mining
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The Missing• CRISP-DM
• Business Understanding• Data Understanding• Data Preparation• Modeling• Evaluation• Deployment
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• CRISP-DM
Source: http://en.wikipedia.org/wiki/File:CRISP-DM_Process_Diagram.png
The Missing
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The Missing• List of Data Mining Techniques/Tools• Suggestion of Appropriate
Techniques to use in a Given Situation
• Example of Data Mining Tool Application
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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
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The Unanswered• Where does Data Mining Most
Benefit the Audit?• Suspected Frauds?• Entire Audit Process?Planning Risk
AssessmentExecution Tests of ControlsReporting Substantive
Tests
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Questions
Given the Benefits of Continuous Auditing, is Data Mining a “Temporary” Solution?
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QuestionsCost-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!
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Questions
Cost-Benefit of Data Mining?
Audit FirmClientSociety (Investor)
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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