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Fraud Detection
Hernan Huwyler
                      Madrid, Spain
                  Fraud Risk Forum
                      January 2013
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
legitimate
                    record
legitimate
  record


             legitimate
               record




     fraudulent record
      out of 9 system flags
Objective:
maximize correct predictions and maintain incorrect
        predictions at an acceptable level
Data Analysis

              Identify Analytics
Internal
  Data



           Apply Analytics to Data
Industry
  Data




            Leads         Refine
Data Analysis              1



                Branch A        Branch C



 Invigilation   Branch B
                           HQ
                                Branch D




  Business      Branch A



                           BI
                                Branch C




 Intiligence    Branch B        Branch D
Data Cleansing Algorithms



            Poor initial data
              conversion        Factiva World Check
                System
             consolidations
                                 World Compliance
           Manual data entry

            Interfaces and       Customized data
                baches            cleansing rules
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
Some techniques
 Statistical data modeling
 Data preprocessing
 Matching algorithms
 Peer group outliers and covariance
 Time-series analysis
Open discussions
 Supervised, unsupervised & hybrid techniques
 Quality data
 Design, implementation and evaluation
 Visualization tools
 E-business transactions
 Best practices

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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