Fraud Detection Using A Database Platform

Fraud Detection Using a
Database Platform

Mike Blakley
Central
Carolina
Chapter of the
Association of
Certified Fraud February 23, 2009
Examiners
Fraud Detetcion using a database platform EZ-R Stats, LLC

Session objectives

Understand why and how
1.
Understand statistical basis for
2.
quantifying differences
Identify ten general tools and
3.
techniques
Understand how pattern
4.
detection fits in
Fraud detection using a database platform EZ-R Stats, LLC

Session agenda and timings

Managing the business risk of fraud (30 minutes)


Overview of statistical approach (10 min)


Discussion of databases (10 min)


Break (10 min)


Details of the approach (40 min)


Brief demo (5 min)


Open discussion and question and answer (15 min)



Handout (CD)

CD with articles and software


PowerPoint presentation


More info at www.ezrstats.com



Optional quiz

Test your understanding


Entirely optional


On home page under “events” – quiz


Results can be e-mailed



“Cockroach” theory of auditing

If you spot one roach….



“Cockroach” theory of auditing

There are probably 30

more that you don’t
see…


Statistics based “roach” hunting

Many frauds coulda/woulda/shoulda been detected with analytics


Overview
Fraud patterns detectable with

digital analysis
 Basis for digital analysis
approach
 Usage examples
 Continuous monitoring
 Business analytics


Objective 1

The Why and How

Three brief examples


ACFE/IIA/AICPA Guidance Paper


Practice Advisory 2320-1


Auditors “Top 10”


Process Overview


Who, What, Why, When & Where



Objective 1a
Example 1
Wake County Transportation Fraud

Supplier Kickback – School Bus

parts
$5 million


Jail sentences


Period of years



Objective 1a

Too little too late

Understaffed internal audit


Software not used


Data on multiple platforms


Transaction volumes large



Objective 1a

Preventable

Need structured, objective

approach
Let the data “talk to you”


Need efficient and effective

approach


Objective 1

Regression Analysis

Stepwise to find

relationships
Forwards
–

Backwards
–

Intervals

Confidence
–

Prediction
–


Objective 1

Data outliers

Sometimes an “out

and out Liar”
But how do you

detect it?


Objective 1

Data Outliers

Plot transportation costs vs.

number of buses
“Drill down” on costs

Preventive maintenance
–

Fuel
–

Inspection
–


Scatter plot with prediction and
confidence intervals


Objective 1a
Example 2
Cost of six types of AIDS drugs

Total Cost of AIDS Drugs

200

150 NDC1
Dollar Amount

NDC2
100
NDC3
50 NDC4
NDC5
0
NDC6
NDC1 NDC2 NDC3 NDC4 NDC5 NDC6
Drug Type


Objective 1

Medicare HIV Infusion Costs
CMS Report for 2005


South Florida - $2.2 Billion


Rest of the country combined -

$.1 Billion


Objective 1
Pareto Chart
Medicare HIV Infusion Costs - 2005 ($Billions)
data source: HHS CMS

120.0%

100.0%
Annual Medicare Costs

80.0%

Pct
60.0%
Cum Pct

40.0%

20.0%

0.0%
1

3

5

7

9

11

13

15
County


Objective 1a
Example 2
Typical Prescription Patterns

AIDS Drugs Prescription Patterns

60.0
NDC1
50.0
NDC2
40.0
Dollar Value

NDC3
30.0
NDC4
20.0
NDC5
10.0 NDC6
0.0
Prov 1 Prov 2 Prov 3 Prov 4 Prov 5 Prov 6
Prescriber


Objective 1a
Example 2
Prescriptions by Dr. X

Dr. X compared with Total Population
350
300
250
Dollar Amount

200
Population
150
100 Dr. X
50
0
NDC1 NDC2 NDC3 NDC4 NDC5 NDC6
Drug Type


Objective 1a
Example 2
Off-label use

Serostim

Treat wasting syndrome, side effect of
–
AIDS, OR
Used by body builders for recreational
–
purposes
One physician prescribed $11.5 million
–
worth (12% of the entire state)


Objective 1a
Example 3
Revenue trends

Overall Revenue Trend

1.2
1.15
Annual Billings

1.1
Overall
1.05
Linear (Overall)
1
0.95
0.9
2001 2002 2003
Calendar Year


Example 3 Objective 1a

Dental Billings

Rapid Increase in Revenues

5
4
Annual Billings

Billings A
($millions)

3
Billings B
2
Linear (Billings A)
1
0
2001 2002 2003
Calendar Year


Objective 1b

Guidance Paper

A proposed implementation approach

“Managing the Business Risk of Fraud: A

Practical Guide” http://tinyurl.com/3ldfza
Five Principles

Fraud Detection

Coordinated Investigation Approach



Objective 1b

Managing the Business Risk of
Fraud: A Practical Guide

ACFE, IIA and AICPA

Exposure draft issued
11/2007, final 5/2008
Section 4 – Fraud

Detection


Guidance Paper

Five Sections

Fraud Risk Governance
–

Fraud Risk Assessment
–

Fraud Prevention
–

Fraud Detection
–

Fraud Investigation and
–
corrective action


Risk Governance

Fraud risk management program


Written policy – management’s expectations

regarding managing fraud risk


Risk Assessment

Periodic review and assessment of potential

schemes and events
Need to mitigate risk



Fraud Prevention

Establish prevention techniques


Mitigate possible impact on the organization



Fraud Detection

Establish detection techniques for fraud


“Back stop” where preventive measures fail,

or
Unmitigated risks are realized



Fraud Investigation and Corrective
Action

Reporting process to solicit input on fraud


Coordinated approach to investigation


Use of corrective action



“60 Minutes” – “World of Trouble”

2/15/09 – Scott Pelley

Fraud Risk Governance – “one grand wink-wink,
–
nod-nod “
Fraud Risk Assessment - categorically false
–

Fraud Prevention – “my husband passed away”
–

Fraud Detection - We didn't know? Never saw one.
–

Fraud Investigation and corrective action - Pick-A-
–
Payment losses $36 billion


Objective 1b

Section 4 – Fraud Detection
Detective Controls


Process Controls


Anonymous Reporting


Internal Auditing


Proactive Fraud Detection



Objective 1b

Proactive Fraud Detection

Data Analysis to identify:

– Anomalies
– Trends
– Risk indicators


Fraud Detective Controls

Operate in the background


Not evident in everyday business

environment
These techniques usually –

Occur in ordinary course of business
–

Corroboration using external information
–

Automatically communicate deficiencies
–

Use results to enhance other controls
–


Examples of detective controls

Whistleblower hot-lines (DHHS and OSA

have them)
Process controls (Medicaid audits and edits)


Proactive fraud detection procedures

Data analysis
–

Continuous monitoring
–

Benford’s Law
–


Objective 1b

Specific Examples Cited

Journal entries – suspicious

transactions
Identification of relationships


Benford’s Law





Objective 1b
Data Analysis enhances ability to
detect fraud

Identify hidden relationships


Identify suspicious transactions


Assess effectiveness of internal

controls
Monitor fraud threats


Analyze millions of transactions



Continuous Monitoring of Fraud
Detection

Organization should develop ongoing

monitoring and measurements
Establish measurement criteria (and

communicate to Board)
Measurable criteria include:



Measurable Criteria – number of

fraud allegations


fraud investigations resolved


Employees attending annual ethics course


Whistle blower allegations


Messages supporting ethical behavior

delivered by executives
Vendors signing ethical behavior standards



Management ownership of each
technique implemented

Each process owner should:

Evaluate effectiveness of technique regularly
–

Adjust technique as required
–

Document adjustments
–

Report modifications needed for techniques which
–
become less effective


Practice Advisory 2320-1
Analysis and Evaluation

International standards for the professional

practice of Internal Auditing
Analytical audit procedures

Efficient and effective
–

Useful in detecting
–
Differences that are not expected


Potential errors


Potential irregularities



Analytical Audit Procedures

May include

– Study of relationships
– Comparison of amounts with
similar information in the
organization
– Comparison of amounts with
similar information in the
industry

Analytical audit procedures

Performed using monetary amounts, physical

quantities, ratios or percentages
Ratio, trend and regression analysis


Period to period comparisons


Auditors should use analytical audit

procedures in planning the engagement


Factors to consider

Significance of the area being audited


Assessment of risk


Adequacy of system of internal control


Availability and reliability of information


Extent to which procedures provide support

for engagement results


Objective 1c

Peeling the Onion

Fraud Items
Possible Error Conditions
Population as Whole


Objective 1d

Fraud Pattern Detection

Round Numbers
Benford’s Law
Market Basket

Stratification Gaps

Target Group

Trend Line Univariate

Duplicates
Holiday
Day of Week


Objective 1e

Digital Analysis (5W)
Who

 What
 Why
 Where
 When


Objective 1e

Who Uses Digital Analysis

Traditionally, IT specialists

With appropriate tools, audit

generalists (CAATs)
Growing trend of business

analytics
Essential component of

continuous monitoring


Objective 1e

What - Digital Analysis

Using software to:

Classify
–

Quantify
–

Compare
–

Both numeric and non-numeric

data


Objective 1e

How - Assessing fraud risk

Basis is quantification


Software can do the “leg work”


Statistical measures of difference

– Chi square
– Kolmogorov-Smirnov
– D-statistic

Specific approaches



Objective 1e

Why - Advantages

Automated process

Handle large data populations

Objective, quantifiable metrics

Can be part of continuous monitoring

Can produce useful business analytics

100% testing is possible

Quantify risk

Repeatable process



Objective 1e

Why - Disadvantages

Costly (time and software costs)


Learning curve


Requires specialized knowledge



Objective 1e

When to Use Digital Analysis

Traditional – intermittent (one off)


Trend is to use it as often as possible




Scheduled processing



Objective 1e

Where Is It Applicable?

Any organization with data in digital

format, and especially if:
Volumes are large
–

Data structures are complex
–

Potential for fraud exists
–


Objective 1

Objective 1 Summarized

Three brief examples


CFE Guidance Paper


“Top 10” Metrics


Process Overview


Who, What, Why, When & Where



Objective 1 - Summarized

1.
Understand statistical basis for quantifying
2.
differences
Identify ten general tools and techniques
3.
Understand use of Excel
4.
How pattern detection fits in
5.

Next is the basis …

Objective 2

Basis for Pattern Detection

Analytical review


Isolate the “significant few”


Detection of errors


Quantified approach



Objective 2

Understanding the Basis

Quantified Approach


Population vs. Groups


Measuring the Difference


Stat 101 – Counts, Totals, Chi

Square and K-S
The metrics used



Objective 2a

Quantified Approach

Based on measureable

differences
 Population vs. Group
 “Shotgun” technique


Objective 2a

Detection of Fraud Characteristics

Something is different than expected



Objective 2b

Fraud patterns

Common theme – “something is

different”
 Groups
 Group pattern is different than
overall population


Objective 2c

Measurement Basis

 Transaction
counts
 Transaction
amounts


Objective 2d

A few words about statistics
(the “s” word)

Detailed knowledge of statistics not

necessary
Software packages do the “number-

crunching”
Statistics used only to highlight

potential errors/frauds
Not used for quantification



Objective 2d

How is digital analysis done?

Comparison of group with population as a

whole
Can be based on either counts or amounts

Difference is measured

Groups can then be ranked using a selected

measure
High difference = possible error/fraud



Demo in Excel of the process

Based roughly on the Wake County

Transportation fraud
Illustrates how the process works, using

Excel


Objective 2d

Histograms

Attributes tallied and categorized into “bins”


Counts or sums of amounts



Objective 2d

Two histograms obtained

Population and group

Population Group

700 80
600 70
60
500
50
400
40
300
30
200
20
100 10
0 0
Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec-
07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07


Objective 2d

Compute Cumulative Amount for each

Count by Month
Cum Pct
80
120.0%
70

100.0%
60

50
80.0%
Count

40
60.0%
30

20 40.0%
10
20.0%
0
Au 07
Ju 7
Fe 7

Ju 7
Ap 7

07
M 07

O7

De 7
No 07
Se 7

0.0%
M7
-0
-0

0

0
0
n-0

0
r-0

l-

c-
p-

v-
n-
b-

ct-
g-
ay
ar
Ja

7
07
07

07

7
07

l-0

-0
M onth

p-
-
n-

-

ov
ay
ar

Ju
Ja

Se
M

N
M


Objective 2d

Are the histograms different?

Two statistical measures of

difference
Chi Squared (counts)

K-S (distribution)

Both yield a difference metric



Objective 2d

Chi Squared

Classic test on data in a table


Answers the question – are the

rows/columns different
Some limitations on when it can be

applied


Objective 2d

Chi Squared

Table of Counts


Degrees of Freedom


Chi Squared Value


P-statistic


Computationally intensive



Objective 2d

Kolmogorov-Smirnov

Two Russian

mathematicians
 Comparison of distributions
 Metric is the “d-statistic”


Objective 2d

How is K-S test done?

Four step process


For each cluster element
1.
determine percentage
Then calculate cumulative
2.
percentage
Compare the differences in
3.
cumulative percentages
Identify the largest difference
4.

Objective 2d - KS

Kolmogorov-Smirnov


Objective 2e

Classification by metrics

Stratification

Day of week

Happens on holiday

Round numbers

Variability

Benford’s Law

Trend lines

Relationships (market basket)

Gaps

Duplicates



Objective e

Auditor’s “Top 10” Metrics

Outliers / Variability
1.
Stratification
2.
Day of Week
3.
Round Numbers
4.
Made Up Numbers
5.
Market basket
6.
Trends
7.
Gaps
8.
Duplicates
9.
Dates
10.


Objective 2

Understanding the Basis

Quantified Approach


Population vs. Groups


Measuring the Difference


Stat 101 – Counts, Totals, Chi Square

and K-S
The metrics used




1.
2.
differences
3.
Understand examples done using Excel
4.
5.

Next are the metrics …


It’s that time!

Session Break!


Objective 3

The “Top 10” Metrics

Overview


Explain Each Metric


Examples of what it can detect


How to assess results



Objective 3

Trapping anomalies


Objective 3

Round Numbers
Benford’s Law
Market Basket

Stratification Gaps

Target Group

Trend Line Univariate

Duplicates
Holiday
Day of Week


1 - Outliers


Outliers are
amounts which
are significantly
different from the
rest of the
population

1 - Outliers


Charting (visual)


Software to analyze “z-scores”


Top and Bottom 10, 20 etc.


High and low variability (coefficient

of variation)


1 - Outliers

Drill down to the group level

Basic statistics

– Minimum, maximum
and average
– Variability

Sort by statistic of interest

– Variability (coefficient
of variation)
– Maximum, etc.


1 - Outliers

Example Results

Provider N Coeff Var
3478421 3,243 342.23
2356721 4,536 87.23
3546789 3,421 23.25
5463122 2,311 18.54

Two providers (3478421 and
2356721) had significantly more
variability in the amounts of their
claims than all the rest.

Next Metric

Outliers
1.
Stratification
2.
Day of Week
3.
Round Numbers
4.
Made Up Numbers
5.
Market basket
6.
Trends
7.
Gaps
8.
Duplicates
9.
Dates
10.


2 - Stratification

Unusual stratification
patterns

Do you
know how
your data
looks?

2 - Stratification

Stratification - How

Charting (visual)

 Chi Squared
 Kolmogorov-Smirnov
 By groups


2 – Stratification

Purpose / types of errors

Transactions out of the ordinary


“Up-coding” insurance claims


“Skewed” groupings


Based on either count or amount




The process?

Stratify the entire population into
1.
“bins” specified by auditor
Same stratification on each group
2.
(e.g. vendor)
Compare the group tested to the
3.
population
Obtain measure of difference for each
4.
group
Sort descending on difference
5.
measure


Units of Service Stratified -
Example Results

Provider N Chi Sq D-stat
2735211 6,011 7,453 0.8453
4562134 8,913 5,234 0.7453
4321089 3,410 342 0.5231
4237869 2,503 298 0.4632

Two providers (2735211 and
4562134) are shown to be much
different from the overall population
(as measured by Chi Square).

3 – Day of Week

Day of Week

Activity on weekdays


Activity on weekends


Peak activity mid to late week



3 – Day of Week

Purpose / Type of Errors

Identify unusually high/low

activity on one or more days of
week
Dentist who only handled

Medicaid on Tuesday
Office is empty on Friday



How it is done?

Programmatically check entire population

Obtain counts and sums by day of week

(1-7)
Prepare histogram

For each group do the same procedure

Compare the two histograms

Sort descending by metric (chi square/d-

stat)


3 – Day of Week

Day of Week - Example Results

Provider N Chi Sq D-stat
2735211 5,404 12,435 0.9802
4562134 5,182 7,746 0.8472
4321089 5,162 87 0.321
4237869 7,905 56 0.2189

Provider 2735211 only provided
service for Medicaid on Tuesdays.
Provider 4562134 was closed on
Thursdays and Fridays.

4 – Round Numbers

Round Numbers

It’s about….
Estimates!


4 – Round Numbers


Isolate estimates


Highlight account numbers in

journal entries with round
numbers
Split purchases (“under the radar”)


Which groups have the most

estimates


4 – Round Numbers

Round numbers

Classify population amounts

– $1,375.23 is not round
– $5,000 is a round number – type 3 (3
zeros)
– $10,200 is a round number type 2 (2
zeros)
Quantify expected vs. actual (d-statistic)

Generally represents an estimate

Journal entries



4 – Round Numbers
Round Numbers in Journal
Entries - Example Results

Account N Chi Sq D-stat
2735211 4,136 54,637 0.9802
4562134 833 35,324 0.97023
4321089 8,318 768 0.321
4237869 9,549 546 0.2189

Two accounts, 2735211 and 4562134
have significantly more round number
postings than any other posting
account in the journal entries.

5 – Made up numbers

Made up Numbers

Curb stoning
Imaginary numbers
Benford’s Law


5 – Made Up Numbers

What can be detected
Made up numbers –

e.g. falsified inventory
counts, tax return
schedules



Benford’s Law using Excel

Basic formula is “=log(1+(1/N))”


Workbook with formulae available at

http://tinyurl.com/4vmcfs
Obtain leading digits using “Left”

function, e.g. left(Cell,1)



Made up numbers

Benford’s Law


Check Chi Square and d-statistic


First 1,2,3 digits


Last 1,2 digits


Second digit


Sources for more info



How is it done?

Decide type of test – (first 1-3 digits, last

1-2 digit etc)
For each group, count number of

observations for each digit pattern
Prepare histogram

Based on total count, compute expected

values
For the group, compute Chi Square and

d-stat
Sort descending by metric (chi square/d-

stat)

Invoice Amounts tested with
Benford’s law - Example Results
Store Hi Digit Chi Sq D-stat
324 79 5,234 0.9802
563 89 4,735 0.97023
432 23 476 0.321
217 74 312 0.2189

During tests of invoices by store, two
stores, 324 and 563 have significantly
more differences than any other store
as measured by Benford’s Law.


6 – Market Basket

Market Basket

Medical “Ping ponging”

Pattern associations

Apriori program

References at end of slides

Apriori – Latin a (from) priori

(former)
Deduction from the known



6 – Market basket


Unexpected patterns and

associations
Based on “market basket” concept


Unusual combinations of diagnosis

code on medical insurance claim


6 – Market basket

Market Basket

JE Accounts

 JE Approvals
 Credit card fraud in Japan –
taxi and ATM


6 – Market basket

How is it done?

First, identify groups, e.g. all

medical providers for a patient
Next, for each provider, assign a

unique integer value
Create a text file containing the

values
Run “apriori” analysis



6 – Market basket

Apriori outputs

For each unique value, probability of

other values
If you see Dr. Jones, you will also

see Dr. Smith (80% probability)
If you see a JE to account ABC, there

will also an entry to account XYZ
(30%)


Next Metric
Outliers
1.
Stratification
2.
Day of Week
3.
Round Numbers
4.
Made Up Numbers
5.
Market basket
6.
Trends
7.
Gaps
8.
Duplicates
9.
Dates
10.


7 - Trends

Trend Busters
Does the pattern make sense?

ACME Technology

30,000
25,000
20,000
Amount

Sales
15,000
Em ployee Count
10,000
5,000
0
7

8
7

M8
7

07
7

08
7
-0

-0
-0

-0
l-0
0

0
v-
n-

n-
p-
ay

ay
ar

ar
Ju

No
Ja

Ja
Se
M

M
M

Date


7 – Trends

Trend Busters

Linear regression


Sales are up, but cost of goods sold is

down
“Spikes”



7 – Trends


Identify trend lines, slopes,

etc.
Correlate trends

Identify anomalies

Key punch errors where

amount is order of
magnitude


7 – Trends

Linear Regression
 Test relationships (e.g.
invoice amount and sales
tax)
 Perform multi-variable
analysis


7 – Trends

How is it done?

Estimate linear trends using “best

fit”
Measure variability (standard

errors)
Measure slope


Sort descending by slope,

variability, etc.


7 – Trends
Trend Lines by Account - Example
Results

Account N Slope Std Err
32451 18 1.230 0.87
43517 17 1.070 4.3
32451 27 1.023 0.85
43517 32 1.010 0.36
43870 23 0.340 2.36
54630 56 -0.560 1.89

Generally the trend is gently sloping
up, but two accounts (43870 and
54630) are different.

8 - Gaps

Numeric Sequence Gaps

What’s there is
interesting, what’s not
there is critical …


8 – Gaps


Missing documents (sales, cash,

etc.)
Inventory losses (missing receiving

reports)
Items that “walked off”



8 – Gaps

How is it done?

Check any sequence of numbers

supposed to be complete, e.g.
Cash receipts


Sales slips


Purchase orders



8 – Gaps

Gaps Using Excel

Excel – sort and check


Excel formula


Sequential numbers and dates



8 – Gaps

Gap Testing - Example Results

Start End Missing

10789 10791 1

12523 12526 2

17546 17548 1

Four check numbers are missing.


9 - Duplicates

Duplicates

Why is there more
than one?
Same, Same, Same, and
Same, Same, Different


9 – Duplicates

Two types of (related) tests

Same items – same vendor, same invoice

number, same invoice date, same amount
Different items – same employee name,

same city, different social security number


9 - Duplicates

Duplicate Payments
High payback area


 “Fuzzy” logic
Overriding software

controls


9 - Duplicates
Fuzzy matching with
software
Levenshtein distance

Soundex

“Like” clause in SQL

Russian
Regular expression physicist

testing in SQL
Vendor/employee

situations


9 - Duplicates

How is it done?

First, sort file in sequence for

testing
Compare items in consecutive

rows
Extract exceptions for follow-up



9 - Duplicates

Possible Duplicates - Example Results

Vendor Invoice Date Invoice Count
Amount

10245 6/15/2007 3,544.78 4

10245 8/31/2007 2,010.37 2

17546 2/12/2007 1,500.00 2

Five invoices may be duplicates.


10 - Dates

Date Checking

If we’re closed, why
is there …
Adjusting journal entry?
Receiving report?
Payment issued?

10 – Dates

Holiday Date Testing
Red Flag indicator



10 – Dates
Date Testing challenges

Difficult to determine


Floating holidays –

Friday, Saturday,
Sunday, Monday


10 – Dates
Typical audit areas

Journal entries


Employee expense

reports
Business telephone calls


Invoices


Receiving reports


Purchase orders



10 – Dates

Determination of Dates

Transactions when business is

closed
Federal Office of Budget

Management
An excellent fraud indicator in

some cases


10 – Dates
Holiday Date Testing

Identifying holiday

dates:
– Error prone
– Tedious

U.S. only



10 – Dates
Federal Holidays

Established by Law


Ten dates


Specific date (unless

weekend), OR
Floating holiday



10 – Dates

Federal Holiday Schedule

Office of Personnel Management

Example of specific date – Independence

Day, July 4th (unless weekend)
Example of floating date – Martin Luther

King’s birthday (3rd Monday in January)
Floating – Thanksgiving – 4th Thursday in

November


10 – Dates

How it is done?

Programmatically count holidays for

entire population
For each group, count holidays


Compare the two histograms (group

and population)
Sort descending by metric (chi

square/d-stat)


10 – Dates

Holiday Counts - Example Results

Employee N Chi Sq D-stat
Number
10245 37 5,234 0.9802
32325 23 4,735 0.97023
17546 18 476 0.321
24135 34 312 0.2189

Two employees (10245 and 32325)
were “off the chart” in terms of
expense amounts incurred on a
Federal Holiday.


1.
2.
differences
3.
4.
5.

Next – using Excel …


Objective 4

Use of Excel

Built-in functions


Add-ins


Macros


Database access



Objective 4

Excel templates
Variety of tests

Round numbers
–

Benford’s Law
–

Outliers
–

Etc.
–


Objective 4

Excel – Univariate statistics

Work with Ranges


=sum, =average, =stdevp


=largest(Range,1),

=smallest(Range,1)
=min, =max, =count


Tools | Data Analysis | Descriptive

Statistics


Objective 4

Excel Histograms

Tools | Data Analysis | Histogram


Bin Range


Data Range



Objective 4

Excel Gaps testing

Sort by sequential value

=if(thiscell-lastcell <>

1,thiscell-lastcell,0)
Copy/paste special

Sort



Objective 4

Detecting duplicates with Excel

Sort by sort values


=if testing


=if(=and(thiscell=lastcell, etc.))



Objective 4

Performing audit tests with macros

Repeatable process

Audit standardization

Learning curve

Streamlining of tests

More efficient and effective

Examples -

http://ezrstats.com/Macros/home.html


Objective 4

Using database audit software

Many “built-in” functions right off the shelf

with SQL
Control totals

Exception identification

“Drill down”

Quantification

June 2008 article in the EDP Audit &

Control Journal (EDPACS) “SQL as an
audit tool”
http://ezrstats.com/doc/SQL_As_An_Audit_Tool.pdf




1.
2.
differences
3.
4.
How Pattern Detection fits in
5.

Next – Fit …

Objective 5

How Pattern Detection Fits In

Business Analytics







Objective 5

Where does Fraud Pattern Detection fit in?

Right in the middle
Business Analytics




Continuous fraud pattern

detection
Continuous Monitoring



Objective 5

Business Analytics

Fraud analytics -> business

analytics
Business analytics -> fraud

analytics


Objective 5

Role in Continuous Monitoring (CM)

Fraud analytics can feed (CM)


Continuous fraud pattern detection


Use output from CM to tune fraud

pattern detection



1.
2.
differences
3.
Understand use of Excel
4.
5.

Next: Links …

Links for more information

Kolmogorov-Smirnov


http://tinyurl.com/y49sec


Benford’s Law http://tinyurl.com/3qapzu


Chi Square tests http://tinyurl.com/43nkdh



http://tinyurl.com/3pltdl


Market Basket

Apriori testing for “ping ponging”

Temple University

http://tinyurl.com/5vax7r
Apriori program (“open source”)

http://tinyurl.com/5qehd5
Article – “Medical ping ponging”

http://tinyurl.com/5pzbh4


Excel macros used in auditing

Excel as an audit software

http://tinyurl.com/6h3ye7
Selected macros -

http://ezrstats.com/Macros/home.html
Spreadsheets forever -

http://tinyurl.com/5ppl7t


Questions?


Contact info

 Phone: (919)-219-1622
 E-mail:
Mike.Blakley@ezrstats.com
 Blog: http://blog.ezrstats.com


Fraud Detection Using A Database Platform

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