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Big Risks Requires Big Data Thinking

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Fight Fraud and Abuse with Forensic Data Visualization by Vincent Walden, EY

Published in: Data & Analytics
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Big Risks Requires Big Data Thinking

  1. 1. Forensic Data Analytics 2015 Big risks requires big data thinking Forensic data analytics use cases Vincent Walden Partner, EY November 17, 2015
  2. 2. Page 2 Agenda ► Key analytics trends in fraud risk management ► “Big data thinking” ► Anti-fraud use case examples: ► Employee and vendor transaction risk scoring ► Payment stream analysis ► Text mining and dashboards to find potentially improper payments ► Social media analytics ► Email analytics, emotive tone and Fraud Triangle Analytics ► Cyber monitoring and events ► Integrating visualization into your risk management platform
  3. 3. Page 3 The forensic data analytics landscape ► The regulators are upping their game ► Be ready - the regulators are investing in advanced monitoring technology ► Big risks requires “big data” thinking ► New approaches to counter fraud and compliance monitoring, beyond simple rules-based tests ► Compliance fatigue? Analytics can help ► Analytics can help improve efficiency and program effectiveness to help compliance functions audit and monitor smarter – saving both time and valuable resources
  4. 4. Page 4 Upping their game: SEC priorities around forensic data analytics -U.S. SEC Chair Mary Jo White, prepared testimony before the Senate Appropriations Subcommittee, May 14, 2014
  5. 5. Page 5 FDA business landscape Data analytics is continued focus area in guidance COSO: Internal Controls Integrated Framework 1. Principal #8: Fraud Risk Assessment (COSO 2013) 2. New guidance coming in December 2015 will have significant focus on the use of proactive forensics data analytics ACFE Report to the Nation on Occupational Fraud 1. For those companies with proactive data analytics in place, the cost per fraud incident was 59.7% lower (roughly $100,000 lower per incident) than those companies not using proactive data analytics – more than any other control listed in the survey. 2. Further, the median duration of fraud based on the presence of proactive data analytics was half the time at 12 months vs 24 months. See 2014 ACFE Report the Nations on Occupational Fraud, Figures 37 and 38
  6. 6. Page 6 Forensic data analytics maturity model Beyond traditional “rules-based queries” – consider all four quadrants False Positive Rate High Low Structured Data Detection Rate Low High Unstructured Data “Traditional” rules-Based Queries & Analytics Matching, Grouping, Ordering, Joining, Filtering Statistical-Based Analysis Anomaly Detection, Clustering Risk Ranking Traditional Keyword Searching Keyword Search Data Visualization & Text Mining Data visualization, drill-down into data, text mining
  7. 7. Page 7 Big data thinking
  8. 8. Page 8 Definition of Big Data Gartner: Big Data is high volume, velocity and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.
  9. 9. Page 9 Big data techniques for counter fraud ► Multiple data sources ► Data visualization ► Text analytics ► Payment/transaction risk scoring ► Predictive modeling – technology assisted monitoring ► Pattern & link analysis ► Flexible deployment models
  10. 10. Page 10 Anti-fraud use case examples
  11. 11. Page 11 Employee risk scoring - Travel & entertainment expense monitoring
  12. 12. Page 12 Vendor risk scoring - potentially improper payments
  13. 13. Page 13 Text mining dashboard - payment descriptions
  14. 14. Page 14 Text mining dashboard – drill down
  15. 15. Page 15 Text mining dashboard – word clouds and stratification
  16. 16. Page 16 Social media analytics
  17. 17. Page 17 Email analytics: Emotive tone – secretive, angry, derogatory emails
  18. 18. Page 18 Email analytics: Fraud triangle analytics Fraud Triangle Analytics: Pressure/Opportunity/Rationalization Employee term analysis Term hit frequency over time
  19. 19. Page 19 Cyber monitoring
  20. 20. Page 20 Surveillance monitoring: executive dashboard ► Aggregate view of risks, by incident ► Quick synopsis of risk profile
  21. 21. Page 21 Surveillance monitoring: management dashboard Risk ranking summary at the trader (employee) level ► Risk score by personnel ► Interactive dashboards
  22. 22. Page 22 Management alert screen Trader alert initiation ► Create customized alerts ► Transparency across multiple data sources: trades, voice, email, chat, entertainment, etc.
  23. 23. Page 23 Trader communication review screen – text analytics using Watson Content Analytics ► Sentiment analysis highlighted using WCA ► Issue coding and tagging
  24. 24. Page 24 Integrating visualization into your risk management platform
  25. 25. Page 25 How is fraud detected? 50% by tip or accident demonstrates the need for improved analytics 2014 ACFE Report to the Nation on Occupational Fraud
  26. 26. Page 26 Start with the “Fraud Tree” of schemes Fraud tree Cash larceny Theft of other assets – inventory/ AR/ fixed assets Revenue recognition Non financial Conflicts of interest Bribery and corruption/ FCPA Illegal gratuities Bid-rigging/ procurement Corruption Fraudulent statements Asset misappropriation Fake vendor Payroll fraud T&E fraud Theft of data GAAP Reserves General focus of auditors General focus of internal auditors General focus of the regulators (opportunity for Auditors and Investigators)
  27. 27. Page 27 Today’s biggest forensic data analytics (FDA) challenges Source: 2014 EY Global Forensic Data Analytics Survey (www.ey.com/fdasurvey) 2% 3% 3% 4% 5% 5% 6% 6% 8% 9% 10% 10% 15% 15% 26% 0% 5% 10% 15% 20% 25% 30% Uncertainty about the relevance of FDA in the Company FDA producing positive results to indicate and prove any fraud or… FDA is not prevalent to the culture Huge volume of data to analyze To identify fraudulent information across large data sets Lack of human resources or manpower to operate FDA Spreading the FDA culture across different Business Units Difficulty in adapting FDA to comply with different regulations in… Poor quality or lack of accuracy in the data To prevent fraud rather than discover fraud FDA is too expensive Convincing senior management or the company about the benefits of… Improving the quality of the analysis process Challenges with combining data across various IT systems Getting the right tools or expertise for FDA
  28. 28. Page 28 Integrating dashboards into an boarder fraud risk management platform Visualization: Detect fraud within a business process Case Management: Assign tasks, flag transactions and delegate projects for review Statistical: Apply fraud insights and automated alerts to take action in real or near time – when it matters Pattern & Link: Uncover hidden fraud and relationships Detect Investigate Respond Discover
  29. 29. Page 29 An enterprise approach, based on solutions Entity and Social Network analytics Predictive analytics Behavioral / Geospatial Prioritized Incidents Business intelligence Context / Text analytics Decision management Content management Case management Forensic analysis Beneficiaries Legal & compliance (including M&A) Internal Audit Big Data, scalable platform, delivered on desktop or mobile device ► Flexible approaches, reports and capabilities for each beneficiary ► Changing risks requires flexible tools ► Knowing “who is who” is key to identifying patterns & opportunities ► Reduced false positives, better ROI ► Cross enterprise view of exposures ► Expedient audits/ investigations ► Data transparency, no “black box” Data Governance and Collaboration Shared Services & Finance BU Leadership & Corporate Internal Sources External Sources Other beneficiaries Enterprise Platform Security intelligence & Cyber Social media feeds Shared svcs. data feeds ERP systems Sanctions & watchlists News feeds & adverse media Internal reports & communications Master & reference data Embedded Intelligence Activity Monitoring Dark Web
  30. 30. Page 30 Five success factors in deploying FDA 1. Focus on the low hanging fruit, the priority of the first project matters 2. Go beyond traditional “rules-based” tests – incorporate big data thinking 3. Communicate: share information on early successes across departments / business units to gain broad support 4. Leadership gets it funded, but interpretation of the results by experienced or trained professionals make the program successful 5. Enterprise-wide deployment takes time, don’t expect overnight adoption
  31. 31. Page 31 Questions or discussion
  32. 32. Page 32 Contact information Vincent Walden Partner, EY 212-773-3643 vincent.walden@ey.com

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