Using Data Analytics to Detect Fraud...© 2018 Association of Certified Fraud Examiners, Inc. 27 of...

© 2018 Association of Certified Fraud Examiners, Inc.

Using Data Analytics to

Detect Fraud

Fundamental Data Analysis Techniques

© 2018 Association of Certified Fraud Examiners, Inc. 2 of 27

Introduction

▪ In determining the 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.


Aging

▪ Analyzing data

based on date

▪ Useful in

examining:

• Accounts

receivable

• Customer

payments

• Accounts payable

• Vendor payments


Application: Aging

Excel ACL IDEA Tableau

▪ Date-based

subtraction

▪ Function

• AGE()

▪ Command• AGE

▪ Functions

• @Age()

• @AgeDateTi

me()

• @AgeTime()

▪ Command

• Aging

▪ DATEDIFF


Applying Filters

▪ Identifies only those

records meeting user-

defined criteria

▪ Used to extract

transactions outside of

expected norm

▪ Can further filter or

analyze results using

additional analysis

techniques


Application: Filters


▪ Advanced

filter

▪ Meta-

tagging

▪ Filter bar

▪ IF

statements

▪ Criteria ▪ IF

▪ Filter shelf


Benchmarking

▪ Comparing a

company’s processes

or performance metrics

to:

• Competitors

• Industry standards

• Historical data

• Budgeted data


Application: Benchmarking

Excel Tableau

▪ Conditional Formatting

▪ Charts

▪ PivotChart

▪ Calculated Fields

▪ Color Legends


Compliance Verification

▪ Determines whether

employee

transactions comply

with company policies

▪ Useful in identifying

whether a company

policy needs to be

either revised or

reinforced


Application: Compliance Verification


▪ IF()

▪ IFError()

▪ Expression

with

conditions

▪ @If()

▪ @CompIf()

▪ IF

▪ ELSEIF

▪ IFNULL


Duplicate Testing

▪ Identifies transactions with duplicate values

in specified fields:

• Check numbers

• Invoice numbers

• Government identification numbers (e.g., Social

Security numbers)

• Employee or vendor addresses


Application: Duplicates

Excel ACL

▪ COUNTIF()

▪ COUNTIFS()

▪ DUPLICATES command

IDEA Tableau

▪ Duplicate Key Detection

command

▪ Duplicate Key Exclusion

command

▪ COUNT

▪ COUNTD

▪ RUNNING_COUNT

▪ WINDOW_COUNT


Expressions and Equations

▪ Build expressions or equations based on

knowledge and expectations of what should

be in the data:

• Recomputing net payroll amounts based on gross

pay, taxes, and other deductions

• Recalculating amounts charged on invoices based

on unit price and quantity ordered


Frequently Used Values

▪ Identifying values

that occur with

unexpected

frequency

▪ Red flag of fictitious

transactions


Application: Frequently Used Values


▪ COUNTIF()

▪ COUNTIFS()

▪ Benford’s

Law

command

▪ Summarize

command

▪ Benford’s Law

command

▪ Summarization

command

▪ COUNTD

▪ SUM of

Number of

Records


Fuzzy Logic Matching

▪ Identifies records with similar or potentially

duplicate—though not identical—values:

• First Street, First St., 1st Street, 1st St.

▪ Helps detect fraud in “gray areas” by

reviewing various iterations of data

▪ Can produce an increased number of false

positives


Application: Fuzzy Logic


▪ Normalize,

then

compare

▪ Fuzzy

Duplicates

command

▪ Normalize,

then

compare

▪ Duplicate

Key Fuzzy

command

▪ Normalize,

then

compare

▪ Normalize,

then compare


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


Application: Gaps


▪ Sort, then

value

comparison

using IF()

▪ GAPS

command

▪ Gap

Detection

command

▪ Calculated

Field

▪ LOOKUP


Graphing

▪ Provides a visual

representation of the

data and can

highlight patterns or

anomalies that might

indicate areas for

further examination


Identifying Amounts Below a Threshold

▪ Search for patterns

of transactions that

fall just below

approval or review

thresholds.


Application: Thresholds


▪ Value

comparison

using IF()

▪ BETWEEN()

function

▪ Value

comparison

using <, >

▪ @Between()

function

▪ Value

comparison

using <, >

▪ IF

▪ Value

comparison

using <, >


Identifying Unusual Dates and Times

▪ Identifies

transactions that

occur during

nonbusiness

hours or

employee

vacations


Application: Unusual Dates and Times

Excel ACL

▪ Value comparison using IF() ▪ NOT BETWEEN() function

▪ Value comparison using <, >

IDEA Tableau

▪ .NOT. @BetweenDate() function

▪ .NOT. @BetweenTime() function

▪ Value comparison using <, >

▪ IF

▪ Value comparison using

<, >


Join/Relate

▪ Combines specified fields from two different

files into a single file using key fields

▪ Looks for matches or discrepancies between

the files


Application: Join/Relate


▪ VLOOKUP()

▪ HLOOKUP()

▪ INDEX()

▪ JOIN

command

▪ RELATE

command

▪ Join

command

▪ Visual

Connector

command

▪ JOIN (at data

import)

▪ UNION (at

data import)

▪ Linking fields


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


Application: Pivot Tables


▪ PivotTable

▪ PowerPivot

▪ Cross-

Tabulate

command

▪ Pivot Table

command

▪ Standard

functionality


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.


Application: Round-Dollar Payments


▪ MOD() ▪ MOD()

function

▪ FIND()

function

▪ @IsInI

function

▪ FIND


Sort/Index

▪ Arranges the data in

ascending or

descending order

based on one or

more specified key

field(s):

• Alphabetically

• Numerically

• Chronologically


Stratification

Invoice amount Count Percent of total Total amount

Less than $1,000 87 10.5% $ 66,078.24

$1,001–$5,000 196 23.6% $ 782,089.00

$5,001–$10,000 359 43.2% $ 2,515,940.21

$10,001–$20,000 102 12.3% $ 1,427,527.74

$20,001–$50,000 68 8.2% $ 2,022,600.16

Over $50,000 19 2.3% $ 1,298,874.96

Total: 831 100% $ 8,113,110.31


Application: Stratification

Excel ACL

▪ SUMIFS() and

COUNTIFS()▪ STRATIFY command

IDEA Tableau

▪ Stratification command ▪ Histogram (bins)


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: 1,086


Application: Summarization

Excel ACL

▪ SUMIFS() and

COUNTIFS()

▪ SUMMARIZE command

▪ CLASSIFY command

IDEA Tableau

▪ Summarization

command

▪ Attribute on Shelf

▪ SUM Number of

Records

Using Data Analytics to Detect Fraud...© 2018 Association of Certified Fraud Examiners, Inc. 27 of...

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