Fraud detection Presentation
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The document discusses fraud detection strategies, highlighting the various sources of fraud detection such as internal audits and suspicions. It emphasizes the importance of data analysis, including the use of analytics, data cleansing, and various statistical techniques to identify and predict fraudulent activities. The content also covers best practices for managing e-business transactions in order to minimize fraud risk.
Fraud detection Presentation
- 1. Fraud Detection Hernan Huwyler Madrid, Spain Fraud Risk Forum January 2013
- 2. Who detects fraud? 33% 18% 14% 13% 6% • Fraud Risk • Internal Audit Management FRM • Suspicions • Tipoff • By —chance” Transaction Reporting Controls PwC's Global economic crime survey 2012
- 3. legitimate record legitimate record legitimate record fraudulent record out of 9 system flags
- 4. Objective: maximize correct predictions and maintain incorrect predictions at an acceptable level
- 5. Data Analysis Identify Analytics Internal Data Apply Analytics to Data Industry Data Leads Refine
- 6. Data Analysis 1 Branch A Branch C Invigilation Branch B HQ Branch D Business Branch A BI Branch C Intiligence Branch B Branch D
- 7. Data Cleansing Algorithms Poor initial data conversion Factiva World Check System consolidations World Compliance Manual data entry Interfaces and Customized data baches cleansing rules
- 8. Demos Vendors / Employees - Conflicts of interests Vendor Activity – Sequentiality and fetching Treasury – Unrecorded Payments Treasury – Abnormalities and triangulations Several sources of data Combined attributes Normalization
- 9. Some techniques Statistical data modeling Data preprocessing Matching algorithms Peer group outliers and covariance Time-series analysis
- 10. Open discussions Supervised, unsupervised & hybrid techniques Quality data Design, implementation and evaluation Visualization tools E-business transactions Best practices