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Procurement Fraud. November 11, 2008. Mike Blakley. Detection and Prevention. Session objectives. Current trends, techniques and best practices Understand statistical basis for analysis Procurement cards (p-cards) Understand use of Excel. Top Six Indicators That you might have a fraud.

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procurement fraud

ProcurementFraud

November 11, 2008

Mike Blakley

Detection and Prevention

session objectives
Session objectives
  • Current trends, techniques and best practices
  • Understand statistical basis for analysis
  • Procurement cards (p-cards)
  • Understand use of Excel
top six indicators that you might have a fraud
Top Six IndicatorsThat you might have a fraud
  • 6. System designed to do “three way match”, but only does two way
  • 5. Procurement software system doesn’t do a match
  • 4. When auditors ask to help them out, they point to the door
  • 3. No procurement software system
  • 2. Procurement clerk drives a Porsche
  • 1. Clerk’s kids drive Porsches between mountain home and beach home
overview
Overview
  • Fraud patterns detectable with digital analysis
  • Basis for digital analysis approach
  • Usage examples
  • Using Excel
the why and how
The Why and How

Objective 1

  • Two brief examples
  • IIA Guidance Paper
  • Auditors “Top 10”
  • Process Overview
  • Who, What, Why, When & Where
example 1 school bus transportation fraud
Example 1School Bus Transportation Fraud

Objective 1

  • Supplier Kickback – School Bus parts
  • $5 million
  • Jail sentences
  • Period of years
regression analysis
Regression Analysis

Objective 1

  • Stepwise to find relationships
    • Forwards
    • Backwards
  • Intervals
    • Confidence
    • Prediction
data outliers
Data outliers

Objective 1

  • Sometimes an “out and out Liar”
  • But how do you detect it?
data outliers1
Data Outliers

Objective 1

  • Plot transportation costs vs. number of buses
  • “Drill down” on costs
    • Preventive maintenance
    • Fuel
    • Inspection
medicare hiv infusion costs
Medicare HIV Infusion Costs

Objective 1

  • CMS Report for 2005
  • South Florida - $2.2 Billion
  • Rest of the country combined - $.1 Billion
pareto chart
Pareto Chart

Objective 1

guidance paper
Guidance Paper

Objective 1

  • A proposed implementation approach
  • “Managing the Business Risk of Fraud: A Practical Guide” http://tinyurl.com/3ldfza
  • Five Principles
  • Fraud Detection
  • Coordinated Investigation Approach
managing the business risk of fraud a practical guide

Objective 1

Managing the Business Risk of Fraud: A Practical Guide
  • IIA, AICPA and ACFE
  • Report issued 5/2008
  • Section 5 – Fraud Detection
section 5 fraud detection
Section 5 – Fraud Detection

Objective 1

  • Detective Controls
  • Process Controls
  • Anonymous Reporting
  • Internal Auditing
  • Proactive Fraud Detection
proactive fraud detection
Proactive Fraud Detection

Objective 1

  • Data Analysis to identify:
    • Anomalies
    • Trends
    • Risk indicators
specific examples cited
Specific Examples Cited

Objective 1

  • Journal entries – suspicious transactions
  • Identification of relationships
  • Benford’s Law
  • Continuous monitoring
data analysis enhances ability to detect fraud

Objective 1

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
peeling the onion
Peeling the Onion

Objective 1c

who uses analytics
Who Uses Analytics

Objective 1e

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

Objective 1e

Analytics – what is it?
  • Using software to:
    • Classify
    • Quantify
    • Compare
  • Both numeric and non-numeric data
how assessing fraud risk

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
why advantages

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
why disadvantages
Why - Disadvantages

Objective 1e

  • Costly (time and software costs)
  • Learning curve
  • Requires specialized knowledge
when to use analytics

Objective 1e

When to Use Analytics
  • Traditional – intermittent (one off)
  • Trend is to use it as often as possible
  • Continuous monitoring
  • Scheduled processing
where is it applicable

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 summarized
Objective 1 Summarized

Objective 1

  • Two brief examples
  • IIA Guidance Paper
  • “Top 10” Metrics
  • Process Overview
objective 1 summarized1
Objective 1 - Summarized
  • Understand why and how
  • Understand statistical basis for quantifying differences
  • Identify ten general tools and techniques

Next is the basis …

basis for pattern detection

Objective 2

Basis for Pattern Detection
  • Analytical review
  • Isolate the “significant few”
  • Detection of errors
  • Quantified approach
understanding the basis

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
quantified approach

Objective 2a

Quantified Approach
  • Based on measureable differences
  • Population vs. Group
  • “Shotgun” technique
detection of fraud characteristics

Objective 2a

Detection of Fraud Characteristics
  • Something is different than expected
fraud patterns

Objective 2b

Fraud patterns
  • Common theme – “something is different”
  • Groups
  • Group pattern is different than overall population
measurement basis

Objective 2c

Measurement Basis
  • Transaction counts
  • Transaction amounts
how is digital analysis done

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
histograms

Objective 2d

Histograms
  • Attributes tallied and categorized into “bins”
  • Counts or sums of amounts
are the histograms different

Objective 2d

Are the histograms different?
  • Two statistical measures of difference
  • Chi Squared (counts)
  • K-S (distribution)
  • Both yield a difference metric
chi squared

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
chi squared1

Objective 2d

Chi Squared
  • Table of Counts
  • Degrees of Freedom
  • Chi Squared Value
  • P-statistic
  • Computationally intensive
kolmogorov smirnov

Objective 2d

Kolmogorov-Smirnov
  • Two Russian mathematicians
  • Comparison of distributions
  • Metric is the “d-statistic”
how is k s test done

Objective 2d

How is K-S test done?
  • Four step process
    • For each cluster element determine percentage
    • Then calculate cumulative percentage
    • Compare the differences in cumulative percentages
    • Identify the largest difference
classification by metrics

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
what can be detected
What can be detected

Objective 2

  • Made up numbers – e.g. falsified inventory counts, tax return schedules
benford s law using excel
Benford’s Law using Excel

Objective 2

  • 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
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
How is it done?

Objective 2

  • 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
Invoice Amounts tested with Benford’s law - Example Results

Objective 2

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.

next metric
Next Metric

Objective 2

  • Outliers
  • Stratification
  • Day of Week
  • Round Numbers
  • Made Up Numbers
  • Market basket
  • Trends
  • Gaps
  • Duplicates
  • Dates
duplicates
Duplicates

Objective 2

Why is there more than one?

Same, Same, Same, and

Same, Same,Different

two types of related tests
Two types of (related) tests

Objective 2

  • Same items – same vendor, same invoice number, same invoice date, same amount
  • Different items – same employee name, same city, different social security number
duplicate payments
Duplicate Payments

Objective 2

  • High payback area
  • “Fuzzy” logic
  • Overriding software controls
fuzzy matching with software
Fuzzy matching with software

Objective 2

  • Levenshtein distance
  • Soundex
  • “Like” clause in SQL
  • Regular expression testing in SQL
  • Vendor/employee situations

Russian physicist

how is it done1
How is it done?

Objective 2

  • First, sort file in sequence for testing
  • Compare items in consecutive rows
  • Extract exceptions for follow-up
possible duplicates example results
Possible Duplicates - Example Results

Objective 2

Five invoices may be duplicates.

next metric1
Next Metric

Objective 2

  • Outliers
  • Stratification
  • Day of Week
  • Round Numbers
  • Made Up Numbers
  • Market basket
  • Trends
  • Gaps
  • Duplicates
  • Dates
holiday date testing
Holiday Date Testing

Objective 2

  • Red Flag indicator
typical audit areas
Typical audit areas

Objective 2

  • Invoices
  • Receiving reports
  • Purchase orders
federal holidays
Federal Holidays

Objective 2

  • Established by Law
  • Ten dates
  • Specific date (unless weekend), OR
  • Floating holiday
understanding the basis1
Understanding the Basis

Objective 2

  • Quantified Approach
  • Population vs. Groups
  • Measuring the Difference
  • Stat 101 – Counts, Totals, Chi Square and K-S
  • The metrics used
objective 2 summarized
Objective 2 - Summarized

Objective 2

  • Understand why and how
  • Understand statistical basis for quantifying differences
  • Procurement cards
  • Understand examples done using Excel

Next up: p-cards …

testing procurement card transactions
Testing Procurement Card Transactions

Objective 3

  • Understand Merchant Charge Codes (MCC)
  • Understand common policies
  • Test procurement card transactions contained on worksheets using VBA
  • Ability to test procurement card transactions in a file using VBA
  • Perform an audit of procurement card transactions in a more efficient and effective manner using the concepts and techniques presented
audit benefits how this test supports the audit
Audit Benefits(How this test supports the audit)

Objective 3

  • Test compliance with policy on an account by account basis
  • Test compliance with policies on account limits
  • Enable 100% testing of transactions
  • Audit process which can be tailored for policy changes
  • Repeatable audit process
mcc structure
MCC Structure

Objective 3

  • Major Categories
  • Airlines 30XX – 32XX
  • Car Rental 33XX, 34XX
  • Hotels 35XX – 37XX
  • All Other
policy structure
Policy Structure

Objective 3

  • Prohibited Codes
  • Codes allowed with a monthly limit
  • Codes allowed without limit
  • Overall card limit
summary and wrap up
Summary and Wrap Up

Objective 3

  • Understand Merchant Charge Codes (MCC)
  • Understand common policies
  • Test procurement card transactions contained on worksheets using VBA
  • Ability to test procurement card transactions in a file using VBA
  • Perform an audit of procurement card transactions in a more efficient and effective manner using the conceptsand techniques presented
objective 3 summarized
Objective 3 - Summarized
  • Understand why and how
  • Understand statistical basis for quantifying differences
  • Procurement cards
  • Understand examples done using Excel

Next up: Excel …

use of excel
Use of Excel

Objective 4

  • Built-in functions
  • Add-ins
  • Macros
  • Database access
excel univariate statistics
Excel – Univariate statistics

Objective 4

  • Work with Ranges
  • =sum, =average, =stdevp
  • =largest(Range,1), =smallest(Range,1)
  • =min, =max, =count
  • Tools | Data Analysis | Descriptive Statistics
excel histograms
Excel Histograms

Objective 4

  • Tools | Data Analysis | Histogram
  • Bin Range
  • Data Range
excel gaps testing
Excel Gaps testing

Objective 4

  • Sort by sequential value
  • =if(thiscell-lastcell <> 1,thiscell-lastcell,0)
  • Copy/paste special
  • Sort
detecting duplicates with excel

Objective 4

Detecting duplicates with Excel
  • Sort by sort values
  • =if testing
  • =if(=and(thiscell=lastcell, etc.))
performing audit tests with macros
Performing audit tests with macros

Objective 4

  • Repeatable process
  • Audit standardization
  • Learning curve
  • Streamlining of tests
  • Examples - http://tinyurl.com/576tp8
use of excel1
Use of Excel

Objective 4

  • Built-in functions
  • Add-ins
  • Macros
objective 4 summarized
Objective 4 - Summarized
  • Understand why and how
  • Understand statistical basis for quantifying differences
  • Identify ten general tools and techniques
  • Understand examples done using Excel
links for more information
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
excel macros used in auditing
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
contact info
Contact info
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