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Smart Solutions: Data Analytics to Support Fraud Examinations

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This is an updated slide set based on my ACFE presentation in 2011. The idea is to present a concept to use Data Analytics in Fraud Investigations. For more information feel free to contact me via www.corma.de.

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Smart Solutions: Data Analytics to Support Fraud Examinations

  1. 1. Smart Solutions: Data Analytics to Support Fraud Investigations
  2. 2. About me Understanding data Cleansing data Data validation & enrichment Importing data Analyzing data Reporting Monitoring Agenda 2
  3. 3. Jörn Weber Certified Fraud Investigator 19 years experience - German law enforcement since1999 Managing Partner at corma GmbH: Solution provider Partner for corporate security About me 3
  4. 4. About corma GmbH 4 Stops suspects by: analytical investigations operative investigations Saves time by: online research online monitoring Increases efficiency & saves money by: data analytics global intelligence solutions
  5. 5. Data Modeling 5 © corma GmbH
  6. 6. Workflow Understanding data Cleansing / Standardizing data Data validation & enrichment Importing data Analyzing data Reporting Monitoring What are “Smart Solutions”? 6
  7. 7. Understanding data 7
  8. 8.  It is a challenge to understand data  What kind of challenge?  Data quantity  Data access  Data integration  Understand relationships & background  Bring data into context Understanding data 8 © Dan Roam
  9. 9.  How does it work?  In four steps Understanding data 9 © Dan Roam
  10. 10. Look at the data: Understanding data 10 © Dan Roam
  11. 11. See the pattern: Understanding data 11 © Dan Roam
  12. 12. Imagine Understanding data 12 © Dan Roam
  13. 13. Show – summaries your findings Understanding data 13 © Dan Roam
  14. 14. Understanding data 14
  15. 15. 1. Chain of Custody • Record all your steps  Software: Hunchly https://www.hunch.ly/  Plain document • Store original data in a secure area • Create “digital fingerprints”: MD5 Hash • Work only with a copy of the original data corma Workflow 15
  16. 16. 2. Identify data format • Research  http://www.file-extensions.org  http://www.filext.com  http://www.fileinfo.com  .gpi  .bqy  .blb Understanding data 16 Garmin Point of Interest file BrioQuery database file ACT! database file
  17. 17. 2. Identify data format • View (read only)  http://www.uvviewsoft.com Understanding data 17
  18. 18. 2. Identify data format • Deep view (editable)  http://www.ultraedit.com Understanding data 18
  19. 19. 3. From raw data to smart structured data Understanding data 19  Develop first ideas for analytical approach
  20. 20. Understanding data 20 Identified & understood data
  21. 21. Understanding data 21 First import & analytics
  22. 22. Workflow Understanding data Cleansing / Standardizing data Data validation & enrichment Importing data Analyzing data Reporting Monitoring What are “Smart Solutions”? 22
  23. 23. Challenges  High data quality required for good analysis results  Constantly increasing data quantity Cleansing/Standardizing data 23
  24. 24. “Poor data quality” samples Cleansing/Standardizing data 24
  25. 25. Why should data be cleansed:  Reliable analysis results are required  Saves time that otherwise would come up during the analysis process  Reduces unwanted deviations & variations  Identify entities (person, organization, address)  Insights often lead to further findings Cleansing/Standardizing data 25
  26. 26. Fast and flexible handling of large quantities of data Flexible import for various data sources Intuitive research Analyses, calculations, statistics Business Intelligence Ad-hoc reporting 26 Solution
  27. 27.  Combine different data formats  Fix data quality issues  Identify missing data  Better link analysis results  Application of different tools for standardized data cleansing 27 Solution
  28. 28. 28 Solution Develop automated queries
  29. 29. 29 Benefits Develop workflow for recurring processes Standardize processes (templates) Benefits:  Time saving  Flexible  Maximize effectiveness  Team “compatibility”  Easy to learn
  30. 30. Workflow Understanding data Cleansing / Standardizing data Data validation & enrichment Importing data Analyzing data Reporting Monitoring What are “Smart Solutions”? 30
  31. 31. Imagine Data validation & enrichment 31
  32. 32. Geocoding: http://www.gpsvisualizer.com Data validation & enrichment 32
  33. 33. Geocoding: http://www.gpsvisualizer.com Data validation & enrichment 33
  34. 34. Geocoding: http://www.gpsvisualizer.com Data validation & enrichment 34
  35. 35. Whois (historical records) Data validation & enrichment 35
  36. 36. Relationships between Entities Data validation & enrichment 36
  37. 37. Visualization & link analysis Data validation & enrichment 37
  38. 38. Address verification – manually Data validation & enrichment 38
  39. 39. Address verification – service provider or software for large amounts of data  AddressDoctor http://www.addressdoctor.com  Experian http://www.qas-experian.com.au Data validation & enrichment 39
  40. 40. Workflow Understanding data Cleansing / Standardizing data Data validation & enrichment Importing data Analyzing data Reporting Monitoring What are “Smart Solutions”? 40
  41. 41. Importing data 41
  42. 42. 42 Sample import: i2 IBM-Database
  43. 43. 43 Case study: Insurance claims audit
  44. 44. Workflow Understanding data Cleansing / Standardizing data Data validation & enrichment Importing data Analyzing data Reporting Monitoring What are “Smart Solutions”? 44
  45. 45. Analytics … yes … but structured:  Identify needed analytical steps  Develop „questions“ to data  What has prompted the need for the analysis?  What is the key question that needs to be answered?  „Create“ evidence out of data  What can you prove?  What do you want to prove?  Visualize your thinking! Analyzing data 45
  46. 46. Analytical techniques  Chronologies and timelines (understand timing and sequence of events)  Sorting (categorizing & hypothesis generation)  Ranking, scoring, prioritizing (determine which items are most important)  Network analysis – analyze relationships between entities (people, organizations, objects) Analyzing data 46
  47. 47. Supporting tools:  Documenting processes in intranet/wiki  Select the right tool for each task  Train the users  Keep the users “busy” Analyzing data 47
  48. 48. Query - an investigative question, converted into database search Analysis Sample: i2 IBM 48
  49. 49. How many organizations are known at this address? Analysis Sample: i2 IBM 49
  50. 50. 50 Analysis Sample (InfoZoom) Decoding (classification, i.e. phone data)
  51. 51. 51 Email analysis with Intella
  52. 52. 52 Timelinemaker i2 IBM Analyst‘s Notebook Timeline Charts
  53. 53. 53 Classic view: Event log View: Event log Explorer Windows event log analysis
  54. 54. 54 Windows event log analysis
  55. 55. Workflow Understanding data Cleansing / Standardizing data Data validation & enrichment Importing data Analyzing data Reporting Monitoring What are “Smart Solutions”? 55
  56. 56. Final works starts when single components are ready Reporting 56
  57. 57. Reporting 57
  58. 58. Workflow Understanding data Cleansing / Standardizing data Data validation & enrichment Importing data Analyzing data Reporting Monitoring What are “Smart Solutions”? 58
  59. 59. Proactively maintain a high, consistent standard of data quality Monitoring 59
  60. 60. 60 Jörn Weber - jw@corma.de +49 (162) 1009402 corma GmbH · Hochstr. 2 · D-41379 Brüggen· Tel: +49 2163 349 0080 · E-Mail: mail@corma.de · Web: www.corma.de Thank you!

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