Internal Investigations Tools Used to Uncover the Truth

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1 Internal Investigations Tools Used to Uncover the Truth October 31, 2012 James A. Snyder, J.D., CFE Managing Partner Forensics & Valuation Services Tom Haldiman, CPA, CFE Supervising Consultant Forensics & Valuation Services

2 Geocoding Tools: Google Earth, Batchgeo 2

3 Data Analytics 3

4 Data Analysis Process 1) Identify & gather relevant data 2) Process that data with an appropriate tool 3) Analyze output generated by that tool 4

5 Search for Conflicts of Interest 1) Identify & gather relevant data -Vendor Listing, Employee Listing, Secretary of State Websites, Land and Property Records, Invoices, , Facebook 2) Process that data with an appropriate tool -Relationship Mapping Software such as IBM i2 Analyst s Notebook, Node XL or Gephi 3) Analyze the output generated by that tool 5

6 Identify and Gather Relevant Data Christopher Andrews is an employee of Diamond Design Jackal, Inc. is a vendor of Diamond Design Jerome Johnston is an owner of Jackal, Inc. Jerome Johnston is also an owner of Imperial Design Imperial Design shares an address with Coldwater Constructors Beth Woodard is an owner of Coldwater Constructors Beth Woodard has the same phone number on file as Jerome Johnston, & they are married Slimline Architects sends invoice to Jackal, Inc., which is then marked up by Jackal, Inc. & sent to Diamond Design Eugene Graham is an owner of Slimline Architects Steve Andrews is the father of Christopher Andrews Steve Andrews is an owner of Imperial Building Imperial Building is a vendor of Diamond Design 6

7 Process that Data with an Appropriate Tool 7

8 Analyze the Output Generated by that Tool 8

9

10

11 11

12 12 Tools: Analyst s Notebook, Node XL, Gephi

13 Relationship Mapping Social Network Analysis Degree Number of links to other entities Find entities who may be the most active in the network based on number of direct links to other entities Betweenness Number of paths through each entity Find gatekeeper entities who may control information flow between different parts of the network Closeness/Centrality Proximity of entity to other entities Find entities who may have the best access to other parts of the network & visibility of activities within the rest of the network Eigenvector (Hubs/Authorities) Closeness to key players Find entities that may have strong influence in the network due to links to other highly active or well connected entities their direct 13

14 Relationship Mapping Social Network Analysis Degree Number of links to other entities Find entities who may be most active in the network based on the number of direct links to other entities 14

15 Relationship Mapping Social Network Analysis Betweenness Number of paths through each entity Find gatekeeper entities who may control information flow between different parts of the network 15

16 Relationship Mapping Social Network Analysis Closeness/Centrality Proximity of entity to other entities Find entities who may have the best access to other parts of network & visibility of activities within the rest of the network 16

17 17

18 Theatre Map 18

19 Theory 1: Tickets Sold at a Price of Zero (Comped) 19

20 Theory 2: Unsold Seats were Actually Sold at Zero Cost, Then Reclassified as Unsold 20

21 Theory 3: Tickets Sold Below Market 21

22 Applicability to Other Companies All Companies Payment detail, payroll detail, vendor listing, employee listing, general ledger detail Manufacturing Cost of goods sold & inventory detail Health care Patient accounting detail Banking Loan maintenance file Every company has unique data that should be considered 22

23 Sequential Checks to the Same Vendor Tools: ACL, Access, Excel, or IDEA 23

24 Missing Checks Tools: ACL, Access, Excel, or IDEA 24

25 Other Data Mining Procedures indicative of a Vendor/Billing Scheme Name similarity (phonetics, acronyms, etc.) Acceleration (systematic spending increases) Employee address matches customer/vendor address Customer Tax ID matches another customer Tax ID Customer/vendor phone number matches employee phone Duplicate invoices or slightly altered attributes Sudden spike in invoice volume or activity Missing contact information (address, phone, names) High volume of transactions ending in 0 or 5 Unusual activity compared to similar vendors or customers Weekend or holiday transaction dates Transactions processed at unusual hours Address is PO Box, maildrop, prison Dormant account suddenly active 25

26 Computer Forensics

27 Computer Forensics Computer forensics (more accurately digital forensics) is the science of recovering digital data in a manner that will withstand courtroom scrutiny Must follow proper chain of custody procedures Must maintain data integrity Not strictly an IT pursuit Typically includes analysis in addition to recovery, which includes review of deleted files, unallocated clusters & metadata 27

28 Not Only Computers 28

29 E-discovery Discovery of evidence in an electronic format, also referred to as electronically stored information (ESI), searched in the course of litigation Covers a variety of data, including documents, , audio files, photos, metadata & more 29

30 Computer Forensics E-Discovery STEP 1: Acquisition Computer Forensics Technician Computer Forensics Technician STEP 2: Analysis/ Review Computer Forensics Technician Lawyers & Other Investigators 30

31 Predictive Coding Analysis & identification of relevant electronic data assisted by artificial intelligence Also known as Technology-Assisted Review 31

32 Growth of Unstructured Data Explosion of text data + Rapid growth in storage capacity + Over 95% of data never leaves the digital domain = Overwhelming volume to review by traditional means 32

33 Evolution: Keyword Search Brute force search for exact, specific term car 33

34 Evolution: Thesaurus search Expansion of original term, but still applied as a brute force search car automobile auto motorcar 34

35 Evolution: Hyper-Thesaurus search Expands to troponyms, hypo/hypernyms, meronyms, entailments, slang, etc tire hood engine headlights chrome car automobile auto garage freeway vehicle parking lot Ford Mustang Chevy A8 motorcar hotrod ride beamer clunker 35

36 Evolution: Concept Searching Performs true latent semantic analysis machine learning of text to do find more like this searches To: Vendor From: Employee Thank you for the gift it looks great in my driveway! I can t wait to take it out on the open road. I promise I won t tell anyone where it came from. s related to topic Documents related to topic 36

37 Evolution: Predictive Coding Uses concept search technology but incorporates statistical predictions of relevance based on observation of & interaction with, the user Senior attorney codes an initial sample set as relevant or not Artificial Intelligence learns from this set & presents another Iterations continue until predictions match reality Alternative approach AI watches reviewers as they code documents & learns from them, then begins presenting predicted relevant documents 37

38 Issues Courts still largely undecided it s a legal minefield Works best on documents rich in text ( , word processing, PDFs, etc.) it doesn t on spreadsheets, databases & others Multi-lingual data sets can cause strange results Our experience has shown it to struggle with jargon, abbreviations, innuendo, sarcasm, slang & effect of emoticons Can only predict relevance based on what you teach it it doesn t have a crystal ball 38

39 Best Uses In litigation, only in large cases & by agreement of both parties & consent of the court Early case assessment (ECA) to rapidly identify relevant material or to cull down corpus to a more manageable size Post-review quality control helps answer What did we miss? Forensic investigations as a supplement to traditional computer forensics & data mining efforts 39

40 Summary 40

41 James A. Snyder J.D., CFE Managing Partner Tom Haldiman CPA, CFE Supervising Consultant

42 BKD Forensics Institute Upcoming Schedule Deciphering Financial Statements: Tips & Tricks for Attorneys o Wednesday, November 7, 2012 o o 11:30 a.m. 12:30 p.m. CST Presented by Jeff Roberts, CPA, CFE, CFF Data Visualization: Taking Your Analytics from Blah to Bam! o Wednesday, November 14, 2012 o o 11:30 a.m. 12:30 p.m. CST Presented by Jeremy Clopton, CPA, ACDA, CFE bkd.com/fi 42

43 Starbucks Gift Card & Certificate To help promote the event as an associate training tool, we are offering a bonus o Associates who attend all three sessions will receive a Starbucks gift card & Certificate of Participation from BKD 43

44 Slides & Webinar Archive Today s presentation slides are available at bkd.com/fi. A recorded copy of the webinar will be available at the same location at the conclusion of webinar series If you have any questions, please contact Dane Thank you for attending today 44

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