Advanced Analytics for GRC

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What can business intelligence, big data, and predictive analytics bring to the world of Governance, Risk, and Compliance? A lot! (Presentation given at joint Deloitte/SAP presentation in Madrid, Spain)

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Advanced Analytics for GRC

  1. 1. Advanced Analytics For GRC: Breaking The Limits Timo Elliott, SAP Timo.Elliott@sap.com @timoelliott
  2. 2. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 2 Agenda  Why Business Analytics?  Analytics Old and New  Big Data and the “4Vs” – Velocity, Volume, Variety, Veracity  Predictive Analytics and Artificial Intelligence  Using These Technologies to Transform GRC  Conclusion
  3. 3. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 3
  4. 4. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 4 Technology Priorities for 2016 and beyond Rank Technology Trend 1 BI/Analytics 2 Cloud 3 Mobile 4 Digitalization / Digital Marketing 5 Infrastructure & Data Center 6 ERP 7 Security 8 Industry-Specific Applications 9 Customer Relationships 10 Networking, Voice, and Data Comms Nine out of eleven years 2006 - 2016 ANALYTICS #1
  5. 5. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 5 Business Priorities What business areas need the most technology support? Source: Gartner, August 2015 Business Analytics
  6. 6. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 6 Business Analytics is the Number One Priority of Finance Source: Gartner, August 2015 “The importance placed on risk management, profitability analysis and reporting, and business intelligence indicates that finance functions continue to want to leverage big data and analytics to broaden how they conceive, organize and perform traditional corporate performance management capabilities.” 2016 Finance Priorities Survey
  7. 7. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 7 Internal Auditors Are Also Turning To More Technology
  8. 8. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 8 There’s a Lot of Opportunity Source: Cangemi, Michael. Staying a Step Ahead: Internal Audit’s Use of Technology, IIA Research Foundation, August 2015 Fewer than 4 out of 10 chief audit executives worldwide feel their departments’ use of technology is appropriate or better.
  9. 9. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 9 Audit Executives Want To Improve Their Big Data / BI Competency
  10. 10. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 10 Tip Internal Audit Management Review By Accident Account Reconciliation Other Document Examination External Audit Notified by Law Enforcement Surveillance/Monitoring IT Controls Confession There Are Big Opportunities – E.g. Fraud Most fraud is typically found without technology today Source: 2016 Report to the Nations on Occupational Fraud and Abuse, Association of Certified Fraud Examiners More often found by accident than by controls or monitoring!
  11. 11. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 11 What Do We Mean By Analytics? First, Reporting… Purchase to Pay Critical data fields Split requisitions and POs Stale requisitions and POs Segregation of duties PO date after invoice date Invoice number sequence Goods received quantity vs. invoice quantity Employee and vendor matches by name and by address Duplicate vendors (by name, address, bank account number) Duplicate purchases (same vendor same invoice number, same amount same GL account) Travel and Entertainment/Purchasing Critical data fields (cardholder master, expense, etc.) Invalid cardholder (no matching employee or terminated employee) Duplicate cardholders (by employee ID or address) Suspicious keyword in the transaction description Declined and disputed transactions Split purchases Duplicate purchases (same merchant same amount) New cardholder watch list/cardholder watch list Ghost card activities Even/small dollar amount transactions Weekend and holiday transactions Potential duplicate reimbursements: e.g. gas with mileage Spending limits on transactions (lavish hotel stays, dinners, etc.) Payroll Critical data fields (payroll master file) Duplicate employees (same bank account or address) Employee status not matching the termination date Hours worked vs. hours paid Employee start date after paycheck date Terminations within 14 days of hire Invalid pay rates (actual/calculated vs. master file) Excessive gross pay Job record deletions (data corrections not using effective date) Delivery quantity vs. sales order quantity Shipment/sales order/price change by an unauthorized employee Cash receipt vs. invoice amount Shipment without a sales order Order to Cash Critical data fields (customer master, sales order, etc.) Duplicate customers (on name or address) Segregation of duties Unauthorized/excessive commissions
  12. 12. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 12 More Automation: “Data Analytics Audits” “We developed analytics around transactional data. A series of scripts were created to flag anomalous transactions, which would then would be subjected to audit procedures. This allowed us to analyze 100% of a population and test the controls around the outliers. In some cases these were used as part of routine audits and in other cases these analytics were designed to highlight red flags for fraud and investigations.” Randa Saleh Chief Audit Executive at Starwood Hotels & Resorts Worldwide, Inc.
  13. 13. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 13 Newer Opportunities: Data Visualization Risk Occurrences By Quarter 254 Risks - 24 % Controls Testing By Status 23 0 contro ls Easy, self-service access to data with tools like SAP Lumira Now with predictive included!
  14. 14. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 14 Insert page title First level Second level  Third level Use Analytics to Optimize Project Success
  15. 15. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 15 Cloud-based Analytics and Visualization SAP BusinessObjects Cloud SAP Cloud Identity Governance etc
  16. 16. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 16 Spatial and Mobile Analytics
  17. 17. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 17 Graph Databases and Network Analysis
  18. 18. 18© 2016 SAP SE or an SAP affiliate company. All rights reserved. SAP Digital Boardroom
  19. 19. 19© 2016 SAP SE or an SAP affiliate company. All rights reserved. Along comes BIG DATA “Vast new streams of data are changing the art of management”
  20. 20. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 20 Finance And Big Data – The Stereotype 43% OF CIOs believe that data is a valuable asset that is being squandered
  21. 21. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 21 IT Underestimates Finance’s Data Awareness 23% 3% CIOs CFOs 9% 52% CIOs CFOs “Does your CFO know what Big Data is?” “Is data on the balance sheet with a monetary value?” More collaboration and communication needed! “No” “Yes”
  22. 22. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 22 Big Data: The Four “Vs” VELOCITY VOLUME VARIETY VERACITY Increasing amount of data generated, ingested, analyzed and managed Increasing speed at which data must be received, processed and understood Beyond traditional structured data sources to “unstructured” data The quality and accuracy of received data
  23. 23. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 23 Can you respond to data requests in under a day? Need to analyze data more quickly Data is hard to find and understand Only 12% 90% agree 58% agree “Finance executives recognize need for speed in data analysis – but few companies are able to deliver in real time.” cfo.com research Financial Velocity
  24. 24. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 24 Velocity: Removing Redundancy in Financial Applications Result: a real-time view of information in the financial system RIGHT NOW
  25. 25. Live Business Velocity
  26. 26. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 26 Savannah Cement “We would discover fraud only after it had happened – at times, even weeks later” Brian Wamwenje, CIO
  27. 27. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 27 Greater Speed Equals Better Business Partnership 89% 76% 94% 62% 54% 43% 50% 25% Reliable or very reliable Valuable or very valuable Effective or very effective analysis Well-aligned or very well-aligned with strategy Do Not Use Use Source: Grant Thornton, APQC FP&A report Influencing Corporate Performance with Stellar Processes, People, and Technology Feb 2015 Impact of rolling forecasts on business evaluation of finance department
  28. 28. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 28 Volume and Variety Shift in Data Sources: Unstructured data growing is at 10x rate of structured data, but it can be hard to store and exploit using traditional IT systems. INVOICES Name Data Type Required? COMPANY_NAME VARCHAR YES INVOICE_ID DECIMAL YES PURCHASE_DATE DATE YES “Structured” Corporate Databases “Unstructured” Data – text, documents, images, social, etc
  29. 29. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 29
  30. 30. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 30 AlertEnterprise Integrate large volumes of structured and semi-structured data from many different systems, in real-time
  31. 31. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 31 Mine text data to spot global supply chain issues
  32. 32. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 32 Veracity DATA DEFINITIONS METADATA DATA INTEGRATION DATA QUALITY DATA AUGMENTATION MASTER DATA Data is >90% of the effort
  33. 33. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 33
  34. 34. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 34 Single source of truth Connect, clean & normalize all relevant data elements Clean, Organize & Normalize
  35. 35. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 35 Real-world example Connect, clean & normalize all relevant data elements Travel System Agency & Supplier.com Credit cards HR/ hierarchy Expense
  36. 36. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 36 Descriptive: What happened? Diagnostic: Why did it happen? Predictive: What will happen? Prescriptive: How can we make it happen? Hindsight Insight Foresight Analytics Maturity
  37. 37. SAN FRANCISCO – This is the year artificial intelligence came into its own for mainstream businesses DATA HARDWARE ALGORITHMS
  38. 38. DATA SCIENCE QUIZ. These numbers were found in two expense claims. One is entirely made up. Which one? EUR 12,- 2.86,- 10.98,- 69,- 29.30,- 3,- 84,- 119.84,- 18.74,- 1.94,- 27,- EUR 93,- 82.65,- 18.46,- 72,- 98.83,- 7.36,- 4.53,- 3,- 8.32,- 48,- 2.94,-
  39. 39. 30.1% 17.6% 12.5% 9.7% 7.9% 6.7% 5.8% 5.1% 4.6% 1 2 3 4 5 6 7 8 9 Benford’s Law Distribution of the first digit of real-world sets of numbers that uniformly span several orders of magnitude
  40. 40. DATA SCIENCE QUIZ. EUR 12,- 2.86,- 10.98,- 69,- 29.30,- 3,- 84,- 119.84,- 18.74,- 1.94,- 27,- EUR 93,- 82.65,- 18.46,- 72,- 98.83,- 7.36,- 4.53,- 3,- 8.32,- 48,- 2.94,- These numbers were found in two expense claims. One is entirely made up. Which one?
  41. 41. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 41 1999 to 2009 “Greece shows the largest deviation from Benford’s law with respect to all measures. [And] the suspicion of manipulating data has officially been confirmed by the European Commission.” Fact and Fiction in EU-Governmental Economic Data, 2011 Euro-Zone Economic Figures Submitted to European Union…
  42. 42. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 42 Putting Benford’s Law to work Accounts payable Estimations in the general ledger Size of inventory among locations Duplicate payments Computer system conversion New combinations of selling prices Customer refunds More data means greater statistical significance for multi- digit tests…
  43. 43. 0.0000 0.0050 0.0100 0.0150 0.0200 0.0250 0.0300 0.0350 0.0400 0.0450 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 Spike at 49 Two first digits of number Percentage Benford’s Law expected Real-Life Banking Example The write-off limit for internal personnel was $5,000. It turned out that the officer was operating with a circle of friends who would apply for credit cards. After they ran up balances of just under $5,000, he would write the debts off…
  44. 44. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 44 Impact of Predictive Analysis 95% 87% 71% 76% 55% 30% Effective Valuable Well-aligned Do Not Use Use Using advanced analytics in Finance results in better alignment, effectiveness, and value Source: Grant Thornton, APQC FP&A report Influencing Corporate Performance with Stellar Processes, People, and Technology Feb 2015
  45. 45. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 45 The Big Opportunity
  46. 46. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 46 Changes and Opportunity “The advent of analytics and artificial technology does not mean the end of human auditors. It means an end to painstaking checking and crossfooting of debit and credit entries and the beginning of auditing careers that thrive on understanding, monitoring, and improving analytical and cognitive systems” World Economic Forum: “75% of respondents thought that 30% of corporate audits will be performed by Artificial Intelligence by 2025” “Eventually, 80 percent of work involved with Sarbanes-Oxley compliance might be automated with analytics.” Source: Deloitte
  47. 47. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 47 SAP FRAUD MANAGEMENT
  48. 48. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 48 SAP Fraud Management • Leverage the power and speed of SAP HANA • Integration into business processes • Alert notification and management • Minimize false positives with real-time simulations • Ability to handle ultra-high volumes of data by leveraging SAP HANA Detection based on rules and predictive analytics to adapt to changing fraud patterns Detect fraud earlier to reduce financial loss Prevent and deter fraud situations Improve the accuracy of detection at less cost
  49. 49. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 49 SAP Fraud Management – Continuous learning Combine top-down & bottom-up approaches to maximize detection effectiveness Expert Knowledge Database Strategy Definition & Calibration Detection Predictive Models Manual Rules Investigation Performance Analysis Top-Down Bottom-Up
  50. 50. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 50 Pattern analysis Pattern analysis - embedded or highly integrated in SAP HANA Big Data Predictive AnalyticsText Search and Mining  Terabytes analyzed at the speed of thought  Compress large data sets into memory  Integrate insights from Hadoop analysis  Unleash the potential of Big Data  Intuitively design and visualize complex predictive models  Bring predictive analytics to everyone in the business  Native full text search  Graphical search modeling  UI toolkit 10101010101 01000101001 10010110110
  51. 51. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 51 Sophisticated Pattern Analysis (*) Based on SAP Predictive Analytics optional offerings
  52. 52. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 52 Situational Awareness What do I need to do right now? Prediction What can I expect to happen? Suggestion What do you recommend? Notification What do I need to know? Perception What’s happening now? Artificial Intelligence Means New Ways of Working… Automation What should I always do? Prevention What can I avoid? Source: Ray Wang, Constellation Research And new, artificial-intelligence powered applications…
  53. 53. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 53 Conclusion: Looking To The Future “A new kind of audit requires a new kind of auditor. It will still be essential for auditors to have a solid foundation in the fundamentals. However, as the auditor’s role becomes more strategic and insightful, audit professionals will need a variety of enhanced skills including strong capabilities and experience with data analytics.” Jon Raphael, Audit Chief Innovation Officer, Deloitte Analytics, Big Data, and Artificial Intelligence allow new ways of working: • Easy, fast access to data, and clear visualizations of exceptions • Ability to examine every transaction, customer and vendor • Reduce manual audit cycles and free up time for more meaningful analysis • Allow the business to do monitoring, not just internal audit
  54. 54. © 2016 SAP SE or an SAP affiliate company. All rights reserved. 54 Thank You! Timo Elliott VP, Global innovation Evangelist Timo.Elliott@sap.com @timoelliott

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