Fraud Detection Using A Database Platform
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Fraud Detetcion using a database platform EZ-R Stats, LLC
Fraud Detection Using a Database Platform
February 23, 2009
Mike BlakleyMike BlakleyCentral Carolina Chapter of the Association of Certified Fraud Examiners
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Fraud detection using a database platform EZ-R Stats, LLC
Session objectives
1. Understand why and how2. Understand statistical basis for
quantifying differences3. Identify ten general tools and
techniques4. Understand how pattern
detection fits in
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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)
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Handout (CD)
CD with articles and software PowerPoint presentation More info at www.ezrstats.com
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Fraud detection using a database platform EZ-R Stats, LLC
Optional quiz
Test your understanding Entirely optional On home page under “events” – quiz Results can be e-mailed
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Fraud detection using a database platform EZ-R Stats, LLC
“Cockroach” theory of auditing
If you spot one roach….
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“Cockroach” theory of auditing
There are probably 30 more that you don’t see…
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Statistics based “roach” hunting
Many frauds coulda/woulda/shoulda been detected with analytics
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Fraud detection using a database platform EZ-R Stats, LLC
Overview Fraud patterns detectable with
digital analysis Basis for digital analysis
approach Usage examples Continuous monitoring Business analytics
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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 1
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Example 1Wake County Transportation Fraud
Supplier Kickback – School Bus parts
$5 million Jail sentences Period of years
Objective 1a
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Too little too late
Understaffed internal audit Software not used Data on multiple platforms Transaction volumes large
Objective 1a
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Preventable
Need structured, objective approach
Let the data “talk to you” Need efficient and effective
approach
Objective 1a
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Regression Analysis
Stepwise to find relationships
– Forwards– Backwards
Intervals– Confidence– Prediction
Objective 1
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Data outliers
Objective 1
Sometimes an “out and out Liar”
But how do you detect it?
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Data Outliers
Plot transportation costs vs. number of buses
“Drill down” on costs– Preventive maintenance– Fuel– Inspection
Objective 1
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Scatter plot with prediction and confidence intervals
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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 A
mou
nt NDC1
NDC2
NDC3
NDC4
NDC5NDC6
Example 2 Objective 1a
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Medicare HIV Infusion Costs
Objective 1
CMS Report for 2005 South Florida - $2.2 Billion Rest of the country combined -
$.1 Billion
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Pareto ChartObjective 1
Medicare HIV Infusion Costs - 2005 ($Billions)data source: HHS CMS
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
120.0%
1 3 5 7 9 11 13 15
County
Annu
al M
edic
are
Cost
s
PctCum Pct
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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
Dolla
r Val
ue
NDC1NDC2NDC3NDC4NDC5NDC6
Example 2 Objective 1a
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Prescriptions by Dr. X
Dr. X compared with Total Population
050
100150200250300350
NDC1 NDC2 NDC3 NDC4 NDC5 NDC6
Drug Type
Dolla
r Am
ount
PopulationDr. X
Example 2 Objective 1a
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Off-label use
Serostim– Treat wasting syndrome, side effect of
AIDS, OR– Used by body builders for recreational
purposes– One physician prescribed $11.5 million
worth (12% of the entire state)
Example 2 Objective 1a
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Revenue trends
Overall Revenue Trend
0.90.95
11.05
1.11.15
1.2
2001 2002 2003
Calendar Year
Annu
al B
illing
s
OverallLinear (Overall)
Example 3 Objective 1a
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Dental Billings
Rapid Increase in Revenues
0
1
2
3
4
5
2001 2002 2003
Calendar Year
Annu
al B
illing
s ($
milli
ons) Billings A
Billings BLinear (Billings A)
Example 3 Objective 1a
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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
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Fraud detection using a database platform EZ-R Stats, LLC
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
Objective 1b
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Guidance Paper
Five Sections– Fraud Risk Governance– Fraud Risk Assessment– Fraud Prevention– Fraud Detection– Fraud Investigation and
corrective action
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Risk Governance
Fraud risk management program Written policy – management’s expectations
regarding managing fraud risk
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Risk Assessment
Periodic review and assessment of potential schemes and events
Need to mitigate risk
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Fraud Prevention
Establish prevention techniques Mitigate possible impact on the organization
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Fraud Detection
Establish detection techniques for fraud “Back stop” where preventive measures fail,
or Unmitigated risks are realized
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Fraud Investigation and Corrective Action
Reporting process to solicit input on fraud Coordinated approach to investigation Use of corrective action
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“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
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Section 4 – Fraud Detection Detective Controls Process Controls Anonymous Reporting Internal Auditing Proactive Fraud Detection
Objective 1b
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Proactive Fraud Detection
Data Analysis to identify:– Anomalies– Trends– Risk indicators
Objective 1b
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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
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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
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Specific Examples Cited
Journal entries – suspicious transactions
Identification of relationships Benford’s Law Continuous monitoring
Objective 1b
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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
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Continuous Monitoring of Fraud Detection
Organization should develop ongoing monitoring and measurements
Establish measurement criteria (and communicate to Board)
Measurable criteria include:
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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
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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
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Practice Advisory 2320-1Analysis 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
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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
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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
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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
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Peeling the Onion
Population as Whole
Possible Error Conditions
Fraud Items
Objective 1c
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Fraud Pattern Detection
Market Basket
Stratification
Trend Line
HolidayDay of Week
Duplicates
Univariate
Gaps
Benford’s LawRound Numbers
Target Group
Objective 1d
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Digital Analysis (5W) Who What Why Where When
Objective 1e
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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
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What - Digital Analysis
Using software to:– Classify– Quantify– Compare
Both numeric and non-numeric data
Objective 1e
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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
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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
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Why - Disadvantages
Costly (time and software costs) Learning curve Requires specialized knowledge
Objective 1e
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When to Use Digital Analysis
Traditional – intermittent (one off) Trend is to use it as often as possible Continuous monitoring Scheduled processing
Objective 1e
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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
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Objective 1 Summarized
Three brief examples CFE Guidance Paper “Top 10” Metrics Process Overview Who, What, Why, When & Where
Objective 1
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Objective 1 - Summarized
1. 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 …
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Basis for Pattern Detection
Analytical review Isolate the “significant few” Detection of errors Quantified approach
Objective 2
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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
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Quantified Approach
Based on measureable differences
Population vs. Group “Shotgun” technique
Objective 2a
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Detection of Fraud Characteristics
Something is different than expected
Objective 2a
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Fraud patterns
Common theme – “something is different”
Groups Group pattern is different than
overall population
Objective 2b
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Measurement Basis
Transaction counts
Transaction amounts
Objective 2c
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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
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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
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Demo in Excel of the process
Based roughly on the Wake County Transportation fraud
Illustrates how the process works, using Excel
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Histograms
Attributes tallied and categorized into “bins” Counts or sums of amounts
Objective 2d
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Two histograms obtained
Population and groupPopulation
0100200300400500600700
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
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Histograms
Attributes tallied and categorized into “bins” Counts or sums of amounts
Objective 2d
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Compute Cumulative Amount for each
Count by Month
0
10
20
30
40
50
60
70
80
Jan-0
7Feb
-07Mar-
07Apr-
07May-
07Ju
n-07Jul
-07
Aug-07
Sep-07
Oct-07Nov-
07Dec
-07
M onth
Coun
t
Cum Pct
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
120.0%
Jan-0
7
Mar-07
May-07
Jul-0
7
Sep-07
Nov-07
Objective 2d
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Are the histograms different?
Two statistical measures of difference
Chi Squared (counts) K-S (distribution) Both yield a difference metric
Objective 2d
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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
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Chi Squared
Table of Counts Degrees of Freedom Chi Squared Value P-statistic Computationally intensive
Objective 2d
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Kolmogorov-Smirnov
Two Russian mathematicians
Comparison of distributions Metric is the “d-statistic”
Objective 2d
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How is K-S test done?
Four step process1. For each cluster element
determine percentage2. Then calculate cumulative
percentage3. Compare the differences in
cumulative percentages4. Identify the largest difference
Objective 2d
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Kolmogorov-Smirnov
Objective 2d - KS
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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
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Auditor’s “Top 10” Metrics
1. Outliers / Variability2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
Objective e
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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
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Objective 2 - 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 are the metrics …
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It’s that time!
Session Break!
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The “Top 10” Metrics
Overview Explain Each Metric Examples of what it can detect How to assess results
Objective 3
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Trapping anomalies
Objective 3
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Fraud Pattern Detection
Market Basket
Stratification
Trend Line
HolidayDay of Week
Duplicates
Univariate
Gaps
Benford’s LawRound Numbers
Target Group
Objective 3
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Outliers / Variability
Outliers are amounts which are significantly different from the rest of the population
1 - Outliers
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Outliers / Variability
Charting (visual) Software to analyze “z-scores” Top and Bottom 10, 20 etc. High and low variability (coefficient
of variation)
1 - Outliers
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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
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Example Results
Provider N Coeff Var
3478421 3,243 342.232356721 4,536 87.233546789 3,421 23.255463122 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
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Next Metric
1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
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Unusual stratification patterns
Do you know how your data looks?
2 - Stratification
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Stratification - How
Charting (visual) Chi Squared Kolmogorov-Smirnov By groups
2 - Stratification
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Purpose / types of errors
Transactions out of the ordinary “Up-coding” insurance claims “Skewed” groupings Based on either count or amount
2 – Stratification
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The process?
1. Stratify the entire population into “bins” specified by auditor
2. Same stratification on each group (e.g. vendor)
3. Compare the group tested to the population
4. Obtain measure of difference for each group
5. Sort descending on difference measure
2 – Stratification
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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-stat2735211 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
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Next Metric
1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
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Day of Week
Activity on weekdays Activity on weekends Peak activity mid to late week
3 – Day of Week
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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
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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)
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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
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Next Metric
1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
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Round Numbers
It’s about….
Estimates!
4 – Round Numbers
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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
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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
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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
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Next Metric
1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
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Made up Numbers
Curb stoning Imaginary numbers
Benford’s Law
5 – Made up numbers
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What can be detected Made up numbers –
e.g. falsified inventory counts, tax return schedules
5 – Made Up Numbers
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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
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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
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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
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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
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Next Metric
1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
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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
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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
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Market Basket
JE Accounts JE Approvals Credit card fraud in Japan –
taxi and ATM
6 – Market basket
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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
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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
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Next Metric
1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
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Trend BustersDoes the pattern make sense?
ACME Technology
05,000
10,00015,00020,00025,00030,000
Jan-0
7Mar-0
7
May-07
Jul-0
7
Sep-07
Nov-07
Jan-0
8Mar-0
8
May-08
Date
Amou
nt Sales
Em ployee Count
7 - Trends
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Trend Busters
Linear regression Sales are up, but cost of goods sold is
down “Spikes”
7 – Trends
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Purpose / Type of Errors
Identify trend lines, slopes, etc.
Correlate trends Identify anomalies Key punch errors where
amount is order of magnitude
7 – Trends
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Linear Regression
Test relationships (e.g. invoice amount and sales tax)
Perform multi-variable analysis
7 – Trends
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How is it done?
Estimate linear trends using “best fit”
Measure variability (standard errors)
Measure slope Sort descending by slope,
variability, etc.
7 – Trends
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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
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Scatter plot with prediction and confidence intervals
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Next Metric
1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
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Numeric Sequence Gaps
What’s there is interesting, what’s not there is critical …
8 - Gaps
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Purpose / Type of Errors
Missing documents (sales, cash, etc.)
Inventory losses (missing receiving reports)
Items that “walked off”
8 – Gaps
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How is it done?
Check any sequence of numbers supposed to be complete, e.g.
Cash receipts Sales slips Purchase orders
8 – Gaps
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Gaps Using Excel
Excel – sort and check Excel formula Sequential numbers and dates
8 – Gaps
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Gap Testing - Example Results
Four check numbers are missing.
Start End Missing
10789 10791 1
12523 12526 2
17546 17548 1
8 – Gaps
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Next Metric
1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
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Duplicates
Why is there more than one?
Same, Same, Same, and
Same, Same, Different
9 - Duplicates
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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
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Duplicate Payments
High payback area “Fuzzy” logic Overriding software
controls
9 - Duplicates
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Fuzzy matching with software
Levenshtein distance Soundex “Like” clause in SQL Regular expression
testing in SQL Vendor/employee
situations
Russian physicist
9 - Duplicates
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How is it done?
First, sort file in sequence for testing
Compare items in consecutive rows
Extract exceptions for follow-up
9 - Duplicates
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Possible Duplicates - Example Results
Five invoices may be duplicates.
Vendor Invoice Date Invoice 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
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Next Metric
1. Outliers2. Stratification3. Day of Week4. Round Numbers5. Made Up Numbers6. Market basket7. Trends8. Gaps9. Duplicates10. Dates
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Date Checking
If we’re closed, why is there …
Adjusting journal entry?Receiving report?
Payment issued?
10 - Dates
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Holiday Date Testing
Red Flag indicator
10 – Dates
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Date Testing challenges
Difficult to determine Floating holidays –
Friday, Saturday, Sunday, Monday
10 – Dates
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Typical audit areas
Journal entries Employee expense
reports Business telephone calls Invoices Receiving reports Purchase orders
10 – Dates
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Determination of Dates
Transactions when business is closed
Federal Office of Budget Management
An excellent fraud indicator in some cases
10 – Dates
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Holiday Date Testing
Identifying holiday dates:– Error prone– Tedious
U.S. only
10 – Dates
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Federal Holidays
Established by Law Ten dates Specific date (unless
weekend), OR Floating holiday
10 – Dates
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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
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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
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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 0.980232325 23 4,735 0.9702317546 18 476 0.32124135 34 312 0.2189
10 – Dates
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The “Top 10” Metrics
Overview Explain Each Metric Examples of what it can detect How to assess results
Objective 3
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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 …
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Use of Excel
Built-in functions Add-ins Macros Database access
Objective 4
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Excel templates
Variety of tests– Round numbers– Benford’s Law– Outliers– Etc.
Objective 4
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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
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Excel Histograms
Tools | Data Analysis | Histogram Bin Range Data Range
Objective 4
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Excel Gaps testing
Sort by sequential value =if(thiscell-lastcell <>
1,thiscell-lastcell,0) Copy/paste special Sort
Objective 4
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Detecting duplicates with Excel
Sort by sort values =if testing =if(=and(thiscell=lastcell, etc.))
Objective 4
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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
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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
Objective 4
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Use of Excel
Built-in functions Add-ins Macros Database access
Objective 4
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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 …
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How Pattern Detection Fits In
Business Analytics Fraud Pattern Detection Continuous monitoring
Objective 5
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Where does Fraud Pattern Detection fit in?
Business Analytics Fraud Pattern Detection Continuous fraud pattern
detection Continuous Monitoring
Right in the middle
Objective 5
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Business Analytics
Fraud analytics -> business analytics
Business analytics -> fraud analytics
Objective 5
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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
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Objective 5 - Summarized
1. 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 …
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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
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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
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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
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Questions?
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Fraud detection using a database platform EZ-R Stats, LLC
Contact info
Phone: (919)-219-1622 E-mail:
[email protected] Blog: http://blog.ezrstats.com