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6 Format of Session 1. Forensic Accounting then and now 2. Overview of Data Analytics 3. Fraud Analytics Basics 4. Advanced Fraud Analytics 5. Data Visualization 6. Wrap-up Question are welcome and encouraged! 6

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8 How Things Used to Be Paper More on-site data collection Volume Types of information Data compilation Fraud focus being more reactive 8

9 How Things are Evolving Move to digital Data in the Cloud More varied sources and types of information Need for different skill sets to deal with the volume and nature of information Proactive exploratory analysis 9

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12 Fighting Fire with Fire 99% of both our problems and solutions come from technology. - Kevin Kelly, Senior Maverick, Wired Magazine 12

13 What is Data Analytics? The science of examining large volumes of data, with the purpose of drawing conclusions about the information. 13

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15 Challenges for Internal Audit Some of the challenging expectation faced by internal audit include: Harnessing and managing the power of data and technology to be more efficient Broadening focus to include Risk Management Delivering more robust and actionable insights Driving change within the business 15

16 What Data Analytics can Do for You Many companies use data analytics in order to: make better business decisions; gather business intelligence; understand industry trends. Data analytics uses sophisticated software tools in order to identify or uncover patterns/relationships and anomalous transactions. 16

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19 Rules-Based Analytics in Internal Audit Help focus audit effort on the key issues Examples: Identify unauthorized payments Identify suspicious journals Identify unauthorized creation, modification, access of an account Identify invalid information Identify duplicate/near-duplicate information Identify extreme deviations from the norm 19

20 Start Your Journey with Rules-Based Analytics Based on your audit objectives, risk assessment, or hypotheses: Identify the data that you have Create rules that are testable against your objectives or hypotheses Run the rules on the data to identify exceptions to investigate 20

21 Simple Rules-Based Analytics Example 1 Risk False vendors Conflicts of interest Rule Identify all vendors with the same address or phone number as an employee. 21

22 Simple Rules-Based Analytics Example 2 Risk Theft of proprietary information Accessing information beyond job requirements Rule Identify all system administrator logins between 9PM and 6AM. 22

23 Intermediate Rules-Based Analytics Example 1 Risk Double-billing by vendors Over-payment to vendors Rule Identify fuzzy duplicates on invoice numbers. Assess whether same vendor 23

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25 Intermediate Rules-Based Analytics Example 2 Risk Inappropriate use of purchase cards Rule Identify purchase card transactions that are outside of what would be considered business use. 25

26 Applied Data Visualization Example 26

27 Basic Does Not Equal Simple Rules-Based Analysis can be used to get sophisticated results Combine more data sets (internal and external) Unstructured data sets Larger data sets 27

28 Skills You Need to do Basic Analytics Data Extraction Data Wrangling Getting the raw data you extracted into a format that you can perform your analysis on 80% of the work Technical Skills Databases Understanding of the Business Communication Skills 28

29 Tools You Need to do Analytics Start with what you have! Excel Data Wrangling Text-to-x conversion (e.g., DATE) Text string manipulation (e.g., CONCATENATE, LEFT, MID, RIGHT, LEN, SEARCH, etc.) Analysis VLOOKUP/HLOOKUP/MATCH/etc. Pivot Tables 29

30 Limitations of Excel Data is easily mutable Can introduce errors in the analysis Data size limitations Summarizing and combining/joining data is cumbersome Hard to control and document your analysis e.g., rolling back analysis if a mistake or data issue is discovered 30

31 Excel Alternatives Microsoft Access Possibly also included on your computer SQL Server or other database software Your IT department may have a license and instance that could be used Open-source analysis software or programming languages R, Python, etc. Proprietary analysis software or programming languages SAS, SPSS, IDEA, ACL 31

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33 What does Advanced Mean? For the purpose of this presentation: Using external or non-traditional data sources with internal data sources Using statistical methods Using data mining methods 33

34 External or Non-Traditional Data Sources GPS Data Fleet Tracking Data Social Media Feeds Wearables Network log data 34

35 Use of Geospatial information Example 1 Mandate Client asked us to review exceptions to policies related to hotel stays and transportation expenses Interesting result: Identified employees that were staying in hotels less than 40km from their own residence. 35

36 Use of Geospatial information Example 2 36

37 Use of Geospatial Information Example 3 Source: 37

38 Use of Non-Traditional Data Source Example Mandate Client asked us to help them analyze whether there was evidence of employees booking time when they were not working Data Sources used: Payroll data Fleet tracking data (GPS + engine data) Car log data (paper) 38

39 Using Statistical Methods Benford s Law Benford s Law is about the frequency distribution of first/leading digits in a natural population of numbers. Natural populations are not bound by minimums or maximums. According to Benford s Law, in a natural population, the first digit is more often 1 than 2, more often 2 than 3, and so on, up to 9. 39

40 Benford s Law 40

41 Using Benford s Law to Detect Bid-Collusion Application to Public Bid Contracts Bid amounts are generally limited by the nature of the work (presence of a maximum value), i.e., Benford s Law does not apply. BUT, the differences between the amounts submitted by the winner of the tender and the 2nd, 3rd, etc. bidder represents a natural population of numbers where Benford s Law is applicable. We compare these differences with Benford's predictions and then refine the analysis by segments (products, years, users, types of services, etc.) 41

42 Bid-Collusion Example 42

43 What is Data Mining? Using computing power to help you find patterns which you might not be able to detect by just looking at the data manually. 43

44 Data Mining Testing Example Examined a database using data mining software Identified if-then rules that applied to the database at least 95% of the time Extracted the exceptions or deviations from these rules such that they could be further reviewed. Results 14 rules were identified. E.g. If NAME is XXXX then COMM_PCT is

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46 What is Data Visualization? the presentation of data in a visual format (e.g. pictorial, graphical), enabling users to grasp difficult analytical concepts, identify new trends or patterns, and gain a better understanding of the underlying data. 46

47 Why is Data Visualization Important? 70% 30% Visualization has been scientifically shown to make it easier to solve problems and make better decisions 47

48 The Risks of Poor Data Visualization Keep it Simple, Stupid! It seems that perfection is reached not when there is nothing to add, but when there is nothing left to take away. -Antoine de Saint Exupéry 48

49 The Risks of Poor Data Visualization 49

50 Poor Data Visualization Truncated Axes Truncating axes on a chart can exaggerate differences in trends, resulting in a misleading representation of the data. 50

51 Poor Data Visualization Dual Axes By adjusting the scaling of each individual axis, data can be shown to correlate when in reality no such correlation exists. Source: Spurious Correlations 51

52 Poor Data Visualization Narrowed Scopes 1 Omitting Data By narrowing the scope of the data presented, visualizations can mislead by failing to provide context and points of reference for interpretation. A common example of this is limited time scope. 52

53 Poor Data Visualization Narrowed Scopes 53

54 Poor Data Visualization Inaccurate Scaling Single-Dimension Scaling on Multiple Dimensions By limiting the scaling of graphical representations to a single dimension, data can be shown with much greater variance than is factually present. This is a frequent error made when using size to denote a specific measure. Source: 54

55 Poor Data Visualization Extraneous Complexity Irrelevant Decoration Adding additional elements to data visualizations (e.g. images, icons, textures) often serve to detract from the impact of the visualization rather than add to it. 3D Effects Adding an additional dimension to charts rarely adds value to viewers of the visualization, and can make the original chart more difficult to interpret. Poor Organization Although standalone visualizations may be well designed, the impact may be impeded by poor presentation or organization. 55

56 Illustrative Examples: Pie Charts 56

57 Illustrative Examples: Effective Use of Colour 1 57

58 Illustrative Examples: Effective Use of Colour 2 Sequential Colours are ordered from Low to High typically to illustrate concentration or strength of a selected measure. 58

59 Illustrative Examples: Effective Use of Colour 3 Diverging Two sequential schemes are arranged to extend from a neutral mid-point, frequently used to illustrate deviations specific ranges (e.g. budget, forecasts). 59

60 Illustrative Examples: Effective Use of Colour 4 Categorical Contrasted colours used to denote different categories of data. Should usually be limited to a maximum of six. 60

61 Illustrative Examples: Effective Use of Colour 5 61

62 Illustrative Examples: Effective Use of Colour 6 62

63 Additional Guiding Principles 63

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72 Internal Audit & Data Analytics Access to all parts of the organization Big picture view of the organization Can see the connections between all the data in the organization Inherently analytic mind-set Business Knowledge Access to senior management and governance 72

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