Fraud DetectionHernan Huwyler                      Madrid, Spain                  Fraud Risk Forum                      Ja...
Who detects fraud?  33%                 18%                                       14%             13%                     ...
legitimate                    recordlegitimate  record             legitimate               record     fraudulent record  ...
Objective:maximize correct predictions and maintain incorrect        predictions at an acceptable level
Data Analysis              Identify AnalyticsInternal  Data           Apply Analytics to DataIndustry  Data            Lea...
Data Analysis              1                Branch A        Branch C Invigilation   Branch B                           HQ ...
Data Cleansing Algorithms            Poor initial data              conversion        Factiva World Check                S...
Demos   Vendors / Employees - Conflicts of interests   Vendor Activity – Sequentiality and fetching   Treasury – Unreco...
Some techniques   Statistical data modeling   Data preprocessing   Matching algorithms   Peer group outliers and covar...
Open discussions   Supervised, unsupervised & hybrid techniques   Quality data   Design, implementation and evaluation...
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Fraud Detection Presentation Forum

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Supervised, unsupervised & hybrid techniques
Quality data
Design, implementation and evaluation
Visualization tools
E-business transactions
Best practices

Published in: Economy & Finance
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Fraud Detection Presentation Forum

  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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