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Fraud DetectionHernan Huwyler Madrid, Spain Fraud Risk Forum January 2013

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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

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legitimate recordlegitimate record legitimate record fraudulent record out of 9 system flags

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Objective:maximize correct predictions and maintain incorrect predictions at an acceptable level

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Data Analysis Identify AnalyticsInternal Data Apply Analytics to DataIndustry Data Leads Refine

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Data Analysis 1 Branch A Branch C Invigilation Branch B HQ Branch D Business Branch A BI Branch C Intiligence Branch B Branch D

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Data Cleansing Algorithms Poor initial data conversion Factiva World Check System consolidations World Compliance Manual data entry Interfaces and Customized data baches cleansing rules

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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

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

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