Detection of Fraud
-
Upload
hedy-hoover -
Category
Documents
-
view
77 -
download
2
description
Transcript of Detection of Fraud
2008 FRAUD FROUMMAY 14-16, 2008 / HYATT REGENCY COCONUT POINT RESORT & SPA / BONITA SPRINGS, FL, USA
Detection ofDetection of FraudFraudPatterns UsingPatterns Using
May 16, 2008
Digital AnalysisDigital Analysis
Slide 2
Session objectives1. Understand why and how2. Understand statistical basis for
quantifying differences3. Identify ten general tools and
techniques4. Understand use of Excel5. How pattern detection fits in
Slide 3
Overview• Fraud patterns detectable with
digital analysis• Basis for digital analysis
approach• Usage examples• Continuous monitoring• Business analytics• Using Excel
Slide 4
The Why and How
• Three brief examples• IIA Guidance Paper• Auditors “Top 10”• Process Overview• Who, What, Why, When &
Where
Objective 1
Slide 5
Example 1Wake County Transportation Fraud
• Supplier Kickback – School Bus parts
• $5 million• Jail sentences• Period of years
Objective 1a
Slide 6
Too little too late
• Understaffed internal audit• Software not used• Data on multiple platforms• Transaction volumes large
Objective 1a
Slide 7
Preventable• Need structured, objective
approach• Let the data “talk to you”• Need efficient and
effective approach
Objective 1a
Slide 8
Cost of six types of AIDS drugs
Total Cost of AIDS Drugs
0
50
100
150
200
NDC1 NDC2 NDC3 NDC4 NDC5 NDC6
Drug Type
Dol
lar
Am
ount NDC1
NDC2
NDC3
NDC4
NDC5
NDC6
Example 2 Objective 1a
Slide 9
Typical Prescription Patterns
AIDS Drugs Prescription Patterns
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Prov 1 Prov 2 Prov 3 Prov 4 Prov 5 Prov 6
Prescriber
Dol
lar
Val
ue
NDC1
NDC2
NDC3
NDC4
NDC5
NDC6
Example 2 Objective 1a
Slide 10
Prescriptions by Dr. X
Dr. X compared with Total Population
050
100150200250
300350
NDC1 NDC2 NDC3 NDC4 NDC5 NDC6
Drug Type
Dol
lar
Am
ount
Population
Dr. X
Example 2 Objective 1a
Slide 11
Off-label use• Serostim
– Treat wasting syndrome, side effect of AIDS, OR
– Used by body builders for recreational purposes
– One physician prescribed $11.5 million worth (12% of the entire state)
Example 2 Objective 1a
Slide 12
Revenue trends
Overall Revenue Trend
0.9
0.95
1
1.05
1.1
1.15
1.2
2001 2002 2003
Calendar Year
Ann
ual B
illin
gs
Overall
Linear (Overall)
Example 3 Objective 1a
Slide 13
Dental Billings
Rapid Increase in Revenues
0
1
2
3
4
5
2001 2002 2003
Calendar Year
Ann
ual B
illin
gs
($m
illio
ns) Billings A
Billings B
Linear (Billings A)
Example 3 Objective 1a
Slide 14
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
Slide 15
Managing the Business Risk of Fraud: A Practical Guide
• Exposure draft of IIA, AICPA and ACFE
• Exposure draft issued 11/2007
• Section 5 – Fraud Detection
Objective 1b
Slide 16
Section 5 – Fraud Detection
• Detective Controls• Process Controls• Anonymous Reporting• Internal Auditing• Proactive Fraud Detection
Objective 1b
Slide 17
Proactive Fraud Detection
• Data Analysis to identify:– Anomalies– Trends– Risk indicators
Objective 1b
Slide 18
Specific Examples Cited
• Journal entries – suspicious transactions
• Identification of relationships• Benford’s Law• Continuous monitoring
Objective 1b
Slide 19
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
Objective 1b
Slide 20
Peeling the Onion
Population as Whole
Possible Error Conditions
Fraud Items
Objective 1c
Slide 21
Fraud Pattern Detection
Market Basket
Stratification
Trend Line
Holiday
Day of Week
Duplicates
Univariate
Gaps
Benford’s Law
Round Numbers
Target Group
Objective 1d
Slide 22
Digital Analysis (5W)
• Who• What• Why• Where• When
Objective 1e
Slide 23
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
Slide 24
What - Digital Analysis• Using software to:
– Classify– Quantify– Compare
• Both numeric and non-numeric data
Objective 1e
Slide 25
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
Slide 26
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
Slide 27
Why - Disadvantages
• Costly (time and software costs)
• Learning curve• Requires specialized
knowledge
Objective 1e
Slide 28
When to Use Digital Analysis
• Traditional – intermittent (one off)
• Trend is to use it as often as possible
• Continuous monitoring• Scheduled processing
Objective 1e
Slide 29
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 1e
Slide 30
Objective 1 Summarized
• Three brief examples• IIA Guidance Paper• “Top 10” Metrics• Process Overview• Who, What, Why, When & Where
Objective 1
Slide 31
Objective 1 - Summarized1. Understand why and how 2. Understand statistical basis for
quantifying differences3. Identify ten general tools and
techniques4. Understand use of Excel5. How pattern detection fits in
Next is the basis …
Slide 32
Basis for Pattern Detection
• Analytical review• Isolate the
“significant few” • Detection of errors• Quantified approach
Objective 2
Slide 33
Understanding the Basis
• Quantified Approach• Population vs. Groups• Measuring the Difference• Stat 101 – Counts, Totals,
Chi Square and K-S• The metrics used
Objective 2
Slide 34
Quantified Approach
• Based on measureable differences
• Population vs. Group• “Shotgun” technique
Objective 2a
Slide 35
Detection of Fraud Characteristics
• Something is different than expected
Objective 2a
Slide 36
Fraud patterns• Common theme –
“something is different”• Groups• Group pattern is different
than overall population
Objective 2b
Slide 37
Measurement Basis
•Transaction counts
•Transaction amounts
Objective 2c
Slide 38
A few words about statistics• 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
Slide 39
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
Objective 2d
Slide 40
Histograms• Attributes tallied and categorized
into “bins”• Counts or sums of amounts
Objective 2d
Slide 41
Two histograms obtained• Population and group
Population
0
100
200
300
400
500
600
700
Jan-07
Feb-07
Mar-07
Apr-07
May-07
Jun-07
Jul-07
Aug-07
Sep-07
Oct-07
Nov-07
Dec-07
Group
01020304050607080
Jan-07
Feb-07
Mar-07
Apr-07
May-07
Jun-07
Jul-07
Aug-07
Sep-07
Oct-07
Nov-07
Dec-07
Objective 2d
Slide 42
Compute Cumulative Amount for each
Count by Month
0
10
20
30
40
50
60
70
80
Month
Cou
ntCum Pct
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
120.0%
Jan-
07
Mar
-07
May
-07
Jul-0
7
Sep-0
7
Nov-0
7
Objective 2d
Slide 43
Are the histograms different?
• Two statistical measures of difference
• Chi Squared (counts)• K-S (distribution)• Both yield a difference
metric
Objective 2d
Slide 44
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
Slide 45
Chi Squared
• Table of Counts• Degrees of Freedom• Chi Squared Value• P-statistic• Computationally intensive
Objective 2d
Slide 46
Kolmogorov-Smirnov
• Two Russian mathematicians
• Comparison of distributions• Metric is the “d-statistic”
Objective 2d
Slide 47
How is K-S test done?• Four step process
1. For each cluster element determine percentage
2. Then calculate cumulative percentage
3. Compare the differences in cumulative percentages
4. Identify the largest difference
Objective 2d
Slide 48
Kolmogorov-SmirnovObjective 2d - KS
Slide 49
Classification by metrics• Stratification• Day of week• Happens on holiday• Round numbers• Variability• Benford’s Law• Trend lines• Relationships (market basket)• Gaps• Duplicates
Objective 2e
Slide 50
Auditor’s “Top 10” Metrics1. Outliers / Variability2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
Objective e
Slide 51
Understanding the Basis
• Quantified Approach• Population vs. Groups• Measuring the Difference• Stat 101 – Counts, Totals, Chi
Square and K-S• The metrics used
Objective 2
Slide 52
Objective 2 - Summarized1. Understand why and how 2. Understand statistical basis for
quantifying differences3. Identify ten general tools and
techniques4. Understand examples done using
Excel5. How pattern detection fits in
Next are the metrics …
Slide 53
The “Top 10” Metrics
• Overview• Explain Each Metric• Examples of what it can detect• How to assess results
Objective 3
Slide 54
Trapping anomaliesObjective 3
Slide 55
Fraud Pattern Detection
Market Basket
Stratification
Trend Line
Holiday
Day of Week
Duplicates
Univariate
Gaps
Benford’s Law
Round Numbers
Target Group
Objective 3
Slide 56
Outliers / VariabilityOutliers are amounts which are significantly different from the rest of the population
1 - Outliers
Slide 57
Outliers / Variability
• Charting (visual)• Software to analyze “z-
scores”• Top and Bottom 10, 20 etc.• High and low variability
(coefficient of variation)
1 - Outliers
Slide 58
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
Slide 59
Example ResultsProvider 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.
1 - Outliers
Slide 60
Next Metric1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
Slide 61
Unusual stratification patterns
Do you know how your data
looks?
2 - Stratification
Slide 62
Stratification - How
• Charting (visual)• Chi Squared• Kolmogorov-Smirnov• By groups
2 - Stratification
Slide 63
Purpose / types of errors• Transactions out of the
ordinary• “Up-coding” insurance
claims• “Skewed” groupings• Based on either count or
amount
2 – Stratification
Slide 64
The process?1. Stratify the entire population into
“bins” specified by auditor2. Same stratification on each group
(e.g. vendor)3. Compare the group tested to the
population4. Obtain measure of difference for
each group5. Sort descending on difference
measure
2 – Stratification
Slide 65
Units of Service Stratified - Example Results
Two providers (2735211 and 4562134) are shown to be much different from the overall population (as measured by Chi Square).
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
2 – Stratification
Slide 66
Next Metric1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
Slide 67
Day of Week
• Activity on weekdays• Activity on weekends• Peak activity mid to late
week
3 – Day of Week
Slide 68
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
3 – Day of Week
Slide 69
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)
Slide 70
Day of Week - Example Results
Provider 2735211 only provided service for Medicaid on Tuesdays. Provider 4562134 was closed on Thursdays and Fridays.
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
3 – Day of Week
Slide 71
Next Metric1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
Slide 72
Round Numbers
It’s about….
Estimates!
4 – Round Numbers
Slide 73
Purpose / Type of Errors• 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
Slide 74
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
Slide 75
Round Numbers in Journal Entries - Example Results
Two accounts, 2735211 and 4562134 have significantly more round number postings than any other posting account in the journal entries.
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
4 – Round Numbers
Slide 76
Next Metric1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
Slide 77
Made up Numbers
Curb stoning
Imaginary numbers Benford’s Law
5 – Made up numbers
Slide 78
What can be detected• Made up numbers
– e.g. falsified inventory counts, tax return schedules
5 – Made Up Numbers
Slide 79
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)
5 – Made Up Numbers
Slide 80
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
5 – Made Up Numbers
Slide 81
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)
5 – Made Up Numbers
Slide 82
Invoice Amounts tested with Benford’s law - Example Results
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.
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
5 – Made Up Numbers
Slide 83
Next Metric1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
Slide 84
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
Slide 85
Purpose / Type of Errors
• Unexpected patterns and associations
• Based on “market basket” concept
• Unusual combinations of diagnosis code on medical insurance claim
6 – Market basket
Slide 86
Market Basket
• JE Accounts• JE Approvals• Credit card fraud in Japan –
taxi and ATM
6 – Market basket
Slide 87
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
Slide 88
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%)
6 – Market basket
Slide 89
Next Metric1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
Slide 90
Trend BustersDoes the pattern make sense?
ACME Technology
05,000
10,00015,00020,00025,00030,000
Date
Am
ount Sales
Employee Count
7 - Trends
Slide 91
Trend Busters
• Linear regression• Sales are up, but cost of
goods sold is down• “Spikes”
7 – Trends
Slide 92
Purpose / Type of Errors
• Identify trend lines, slopes, etc.
• Correlate trends• Identify anomalies• Key punch errors where
amount is order of magnitude
7 – Trends
Slide 93
Linear Regression•Test relationships (e.g.
invoice amount and sales tax)
•Perform multi-variable analysis
7 – Trends
Slide 94
How is it done?• Estimate linear trends
using “best fit”• Measure variability
(standard errors)• Measure slope• Sort descending by slope,
variability, etc.
7 – Trends
Slide 95
Trend Lines by Account - Example Results
Generally the trend is gently sloping up, but two accounts (43870 and 54630) are different.
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
7 – Trends
Slide 96
Next Metric1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
Slide 97
Numeric Sequence Gaps
What’s there is interesting, what’s not there is critical …
8 - Gaps
Slide 98
Purpose / Type of Errors• Missing documents (sales,
cash, etc.)• Inventory losses (missing
receiving reports)• Items that “walked off”
8 – Gaps
Slide 99
How is it done?• Check any sequence of
numbers supposed to be complete, e.g.
• Cash receipts• Sales slips• Purchase orders
8 – Gaps
Slide 100
Gaps Using Excel
• Excel – sort and check• Excel formula• Sequential numbers and
dates
8 – Gaps
Slide 101
Gap Testing - Example Results
Four check numbers are missing.
Start End Missing
10789 10791 1
12523 12526 2
17546 17548 1
8 – Gaps
Slide 102
Next Metric1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
Slide 103
Duplicates
Why is there more than one?
Same, Same, Same, and
Same, Same, Different
9 - Duplicates
Slide 104
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
Slide 105
Duplicate Payments
• High payback area
•“Fuzzy” logic• Overriding
software controls
9 - Duplicates
Slide 106
Fuzzy matching with software
• Levenshtein distance• Soundex• “Like” clause in SQL• Regular expression
testing in SQL• Vendor/employee
situations
Russian physicist
9 - Duplicates
Slide 107
How is it done?
• First, sort file in sequence for testing
• Compare items in consecutive rows
• Extract exceptions for follow-up
9 - Duplicates
Slide 108
Possible Duplicates - Example Results
Five invoices may be duplicates.
VendorInvoice
DateInvoice Amount Count
10245 6/15/2007 3,544.78 4
10245 8/31/2007 2,010.37 2
17546 2/12/2007 1,500.00 2
9 - Duplicates
Slide 109
Next Metric1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
Slide 110
Date Checking
If we’re closed, why is there …
Adjusting journal entry
Receiving report
10 - Dates
Slide 111
Holiday Date Testing• Red Flag indicator
10 – Dates
Slide 112
Date Testing challenges• Difficult to
determine• Floating holidays
– Friday, Saturday, Sunday, Monday
10 – Dates
Slide 113
Typical audit areas• Journal entries• Employee expense
reports• Business telephone
calls• Invoices• Receiving reports• Purchase orders
10 – Dates
Slide 114
Determination of Dates
• Transactions when business is closed
• Federal Office of Budget Management
• An excellent fraud indicator in some cases
10 – Dates
Slide 115
Holiday Date Testing
• Identifying holiday dates:–Error prone–Tedious
• U.S. only
10 – Dates
Slide 116
Federal Holidays
• Established by Law• Ten dates• Specific date (unless
weekend), OR• Floating holiday
10 – Dates
Slide 117
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
Slide 118
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
Slide 119
Holiday Counts - Example Results
Two employees (10245 and 32325) were “off the chart” in terms of expense amounts incurred on a Federal Holiday.
Employee Number N Chi Sq D-stat
10245 37 5,234 0.9802
32325 23 4,735 0.97023
17546 18 476 0.321
24135 34 312 0.2189
10 – Dates
Slide 120
The “Top 10” Metrics• Overview• Explain Each Metric• Examples of what it can
detect• How to assess results
Objective 3
Slide 121
Objective 3 - Summarized
1. Understand why and how 2. Understand statistical basis for quantifying
differences3. Identify ten general tools and techniques4. Understand examples done using Excel5. How pattern detection fits in
Next – using Excel …
Slide 122
Use of Excel• Built-in functions• Add-ins• Macros• Database access
Objective 4
Slide 123
Excel templates• Variety of tests
– Round numbers– Benford’s Law– Outliers– Etc.
Objective 4
Slide 124
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
Slide 125
Excel Histograms
• Tools | Data Analysis | Histogram
• Bin Range• Data Range
Objective 4
Slide 126
Excel Gaps testing• Sort by sequential
value• =if(thiscell-lastcell <>
1,thiscell-lastcell,0)• Copy/paste special• Sort
Objective 4
Slide 127
Detecting duplicates with Excel
• Sort by sort values• =if testing• =if(=and(thiscell=lastcell,
etc.))
Objective 4
Slide 128
Performing audit tests with macros
• Repeatable process• Audit standardization• Learning curve• Streamlining of tests• Examples -
http://tinyurl.com/576tp8
Objective 4
Slide 129
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”
Objective 4
Slide 130
Use of Excel
• Built-in functions• Add-ins• Macros• Database access
Objective 4
Slide 131
Objective 4 - Summarized
1. Understand why and how 2. Understand statistical basis for quantifying
differences3. Identify ten general tools and techniques4. Understand examples done using Excel5. How Pattern Detection fits in
Next – Fit …
Slide 132
How Pattern Detection Fits In• Business Analytics• Fraud Pattern
Detection• Continuous monitoring
Objective 5
Slide 133
Where does Fraud Pattern Detection fit in?
• Business Analytics• Fraud Pattern Detection• Continuous fraud
pattern detection• Continuous Monitoring
Right in the middle
Objective 5
Slide 134
Business Analytics
• Fraud analytics -> business analytics
• Business analytics -> fraud analytics
Objective 5
Slide 135
Role in Continuous Monitoring (CM)
• Fraud analytics can feed (CM)
• Continuous fraud pattern detection
• Use output from CM to tune fraud pattern detection
Objective 5
Slide 136
Objective 5 - Summarized1. Understand why and how 2. Understand statistical basis for
quantifying differences3. Identify ten general tools and
techniques4. Understand use of Excel5. How pattern detection fits in
Next: Links …
Slide 137
Links for more information• Kolmogorov-Smirnov • http://tinyurl.com/y49sec• Benford’s Law
http://tinyurl.com/3qapzu• Chi Square tests
http://tinyurl.com/43nkdh• Continuous monitoring
http://tinyurl.com/3pltdl
Slide 138
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
Slide 139
Excel macros used in auditing
• Excel as an audit software http://tinyurl.com/6h3ye7
• Selected macros - http://tinyurl.com/576tp8
• Spreadsheets forever - http://tinyurl.com/5ppl7t
Slide 140
Questions?