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Fraud Detection presentation

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Statistical data modeling
Data preprocessing
Matching algorithms
Peer group outliers and covariance
Time-series analysis

Published in: Economy & Finance
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Fraud Detection presentation

  1. 1. Fraud DetectionHernan Huwyler Madrid, Spain Fraud Risk Forum January 2013
  2. 2. Who detects fraud? 33% 18% 14% 13% 6% • Fraud Risk • Internal Audit Management FRM • Suspicions• Tipoff • By —chance” Transaction Reporting Controls PwCs Global economic crime survey 2012
  3. 3. legitimate recordlegitimate record legitimate record fraudulent record out of 9 system flags
  4. 4. Objective:maximize correct predictions and maintain incorrect predictions at an acceptable level
  5. 5. Data Analysis Identify AnalyticsInternal Data Apply Analytics to DataIndustry Data Leads Refine
  6. 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. 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. 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. 9. Some techniques Statistical data modeling Data preprocessing Matching algorithms Peer group outliers and covariance Time-series analysis
  10. 10. Open discussions Supervised, unsupervised & hybrid techniques Quality data Design, implementation and evaluation Visualization tools E-business transactions Best practices

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