1. A RESEARCH TAXONOMY: THE APPLICATION OF DATA MINING TO FRAUD DETECTION Glen L. Gray California State University at Northridge Roger Debreceny University of Hawai‘i at Mānoa 8th Biennial Symposium on Information Integrity and Information Systems Assurance October, 2013

2. Introduction Observation The application of data mining to fraud detection during financial audits is at an early stage of development and researchers take a scatter-shot approach Objectives of our study Explore the application of data mining techniques to fraud detection Develop a taxonomy to support and guide future research

3. Data Examination Tools Data Analysis Data Mining Data Extraction & Query Software sophistication Predictive Power

4. GRAB THE LOW HANGING FRUIT..

5. Return on investment in data mining Spread investment in data mining over many clients

6. Return on investment in data mining Spread investment in data mining for one client over many possible fraud objects

7. LOOKING FOR THE SWEET SPOT.. Where can we leverage value from data mining in fraud detection?

8. Looking for the sweet spot! Scheme Fraud

9. Looking for the sweet spot!

11. Fraud Class by Evidence Scheme Gao, L., and R. P. Srivastava. 2011. The Decomposition of Management Fraud Schemes: Analyses and Implications. Indian Accounting Review 15 (1):1-23.

12. Audit Specific Data Mining Scoring Scheme Source Target Signals Data Types Semantics Scoring Elements

13. Fraud Class, Evidence Scheme and Data Mining

14. FUTURE RESEARCH

15. Themes in Data Mining Mining External Information as Part of the Planning Phase Mining Client Non-financial performance data Analysis of Journal Entries Mining Accounting Information Systems Email and other textual sources