Using Data Analytics to Detect Fraud

Transcription

1 Using Data Analytics to Detect Fraud Fundamental Data Analysis Techniques 2016 Association of Certified Fraud Examiners, Inc.

2 Discussion Question For each data analysis technique discussed in this section, identify a few fraud scenarios that could be detected or investigated using that technique Association of Certified Fraud Examiners, Inc. 2 of 27

3 Introduction In determining types of tests to run, consider: The particular fraud risks that are present The data available to work with The type of predication that exists Often, techniques are most effective when used in combination Association of Certified Fraud Examiners, Inc. 3 of 27

4 Aging Analyzing data based on date Useful in examining: Accounts receivable Customer payments Accounts payable Vendor payments 2016 Association of Certified Fraud Examiners, Inc. 4 of 27

5 Applying Filters Identifies only those records meeting userdefined criteria Used to extract transactions outside of expected norm Results can be filtered further or subject to additional analysis 2016 Association of Certified Fraud Examiners, Inc. 5 of 27

6 Benchmarking Comparing a company s processes or performance metrics to: Competitors Industry standards Historical data Budgeted data 2016 Association of Certified Fraud Examiners, Inc. 6 of 27

7 Compliance Verification Determines whether employee transactions comply with company policies Useful in identifying whether a company policy needs to be either revised or reinforced 2016 Association of Certified Fraud Examiners, Inc. 7 of 27

8 Duplicate Testing Identifies transactions with duplicate values in specified fields: Check numbers Invoice numbers Social Security numbers Employee or vendor addresses 2016 Association of Certified Fraud Examiners, Inc. 8 of 27

9 Expressions and Equations Build expressions or equations based on knowledge and expectations of what should be in the data: Re-computing net payroll amounts based on gross pay, taxes, and other deductions Recalculating amounts charged on invoices based on unit price and quantity ordered 2016 Association of Certified Fraud Examiners, Inc. 9 of 27

10 Frequently Used Values Identifying values that occur with unexpected frequency Red flag of fictitious transactions 2016 Association of Certified Fraud Examiners, Inc. 10 of 27

11 Fuzzy Logic Matching Identifies records with similar or potentially duplicate though not identical values: First Street, First St., 1 st Street, 1 st St. Helps detect fraud in gray areas by reviewing various iterations of data Can produce an increased number of false positives 2016 Association of Certified Fraud Examiners, Inc. 11 of 27

12 Gap Tests Search for missing items in a series or sequence of consecutive numbers: Check numbers Invoice numbers Purchase order numbers Inventory tags Search for sequences where none are expected: Social Security numbers 2016 Association of Certified Fraud Examiners, Inc. 12 of 27

13 Provides a visual representation of the data and can highlight patterns or anomalies that might indicate areas for further examination Graphing 2016 Association of Certified Fraud Examiners, Inc. 13 of 27

14 Identifying Amounts Below a Threshold Search for patterns of transactions that fall just below approval/review thresholds Association of Certified Fraud Examiners, Inc. 14 of 27

15 Identifying Unusual Dates and Times Identifies transactions that occur during nonbusiness hours or employee vacations 2016 Association of Certified Fraud Examiners, Inc. 15 of 27

16 Join/Relate Combines specified fields from two different files into a single file using key fields Looks for matches or discrepancies between the files 2016 Association of Certified Fraud Examiners, Inc. 16 of 27

17 Pivot Tables Interactive data summarization tool used to sort, count, total, or give the average of specified data in a spreadsheet Can perform the filter and sort functions within the pivot table Helpful way to see the big picture of the data 2016 Association of Certified Fraud Examiners, Inc. 17 of 27

18 Round-Dollar Payments Most real-world cash transactions do not occur in simple round numbers. Unusual amounts or regular occurrences of round-dollar payments can be red flags of fraud Association of Certified Fraud Examiners, Inc. 18 of 27

19 Sort/Index Arranges the data in ascending or descending order based on one or more specified key field(s) Alphabetically Numerically Chronologically 2016 Association of Certified Fraud Examiners, Inc. 19 of 27

20 Stratification Invoice amount Count Percent of total Total amount Less than $1, % $ 66, $1,001 $5, % $ 782, $5,001 $10, % $ 2,515, $10,001 $20, % $ 1,427, $20,001 $50, % $ 2,022, Over $50, % $ 1,298, Total: % $ 8,113, Association of Certified Fraud Examiners, Inc. 20 of 27

21 Summarization Counting the number of records with common values within a specified field State Count Texas 704 Florida 362 Georgia 12 New Hampshire 1 Virginia 7 Total: 1, Association of Certified Fraud Examiners, Inc. 21 of 27