Más contenido relacionado Similar a Using Data Analytics to Find and Deter Procure to Pay Fraud (20) Using Data Analytics to Find and Deter Procure to Pay Fraud2. 3/11/2014
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About Peter Goldmann, MSc., CFE
President and Founder of White Collar Crime 101
• Publisher of White-Collar Crime Fighter
• Developer of FraudAware® Anti-Fraud Training
• Monthly Columnist, The Fraud Examiner, ACFE
Newsletter
Member of Editorial Advisory Board, ACFE
Author of “Fraud in the Markets”
• Explains how fraud fueled the financial crisis.
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Richard B. Lanza, CPA, CFE, CGMA
• Over two decades of ACL and Excel software usage
• Wrote the first practical ACL publication on how to
use the product in 101 ways (101 ACL
Applications)
• Has written and spoken on the use of audit data
analytics for over 15 years.
• Received the Outstanding Achievement in Business
Award by the Association of Certified Fraud
Examiners for developing the publication
Proactively Detecting Fraud Using Computer Audit
Reports as a research project for the IIA
• Recently was a contributing author of:
• Global Technology Audit Guide (GTAG #13) Fraud
in an Automated World - IIA
• Data Analytics – A Practical Approach - research
whitepaper for the Information System
Accountability Control Association.
• “Cost Recovery – Turning Your Accounts Payable
Department into a Profit Center” – Wiley & Sons.
Please see full bio at www.richlanza.com
3. 3/11/2014
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Disclaimers
4. 3/11/2014
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Today’s Agenda
Costliest procurement frauds to beware of (with
case studies)
Indicators of possible collusive procurement-
vendor schemes
Red flags to look for in audits of procurement
using data analytics
How to conduct the audit using data analytics
What to do if you find vendor or procurement
fraud during an audit
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Issues in Vendor Master
Management
Too many active vendors
(with no activity)
Improper segregation
of duties
Difficult to understand or
improve spend
management
Difficult to manage or
improve payment terms
Invalid Tax IDs leading to B
notices
Too many active vendors
(with no activity)
Improper segregation
of duties
Difficult to understand or
improve spend
management
Difficult to manage or
improve payment terms
Invalid Tax IDs leading to B
notices
Duplicate vendors
representing parents and
subs, for different
companies, or just
mistakes = duplicate pays
Duplicate vendors
representing parents and
subs, for different
companies, or just
mistakes = duplicate pays
Duplicate vendors
representing parents and
subs, for different
companies, or just
mistakes = duplicate pays
Vendors are confused and
may see opportunities to
take advantage of the
company
Vendors are confused and
may see opportunities to
take advantage of the
company
Lack of vendor
management …increases
opportunity for fraud
Lack of vendor
management …increases
opportunity for fraud
Lack of vendor
management …increases
opportunity for fraud
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Best Practice Approach on
Fraud:Find It Before It Finds You
Think Prevention vs. Future Detection
Identify issues earlier in their lifecycle
Build a aura of deterrence within procure to pay
Build a Continuous Review Process
Technology
Minimal staff time / external assistance
Keep Improving the Model
Model on identified frauds
Remove false positives to isolate interesting events
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Analytic Command Center
Analytic Command Center
1. Accounts Payable
2. Accounts Receivable
3. Financial Statement
4.General Ledger
5. Inventory
6.Payroll
7. Revenue
Data Mart
Local Analytic
Toolkit
Feedback from
all locations
Recovery Auditors
Shared Services
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The Overall Fraud
Analytic Process
Get the Most Useful Data for Analysis
General Ledger / Accounts Payable
Other? / Use external data sources
Develop Fraud Query Viewpoints
The 5 Dimensions
Brainstorm report ideas
Analytically Trend
Benford’s Law
Statistical averages and simple trending by day, month, day of week
Post dated changes
Use Visualization Techniques
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Clear Data Request
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Fraud Data Considerations for
the P2P Cycle
A/P and G/L Review Factors
Accounts that are sole sourced
Accounts that have too many vendors
Categories that map to the “recovery list”
Assess to industry cost category benchmarks
Top 100 vendors
Trend analysis over time
Trend analysis by vendor (scatter graph)
Purchase Order / Price List
Match to invoice payments to assess price
differences
Strategic sourcing vendor review
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Distribution Analysis
Remove subtotals for improved visibility
Focus on sole source and multi source vendors
Scroll out and drill to details as needed
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Number Ranking
Summarize each amount (Pivot or ACL)
Rank each number in order of occurrence
Score each item in a sliding scale
May be easiest to use a stratified score
Decide if unique is weirder than non-unique
Relate this summarized list back to the original
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Some Data Mining Approaches
Personnel Analysis
Adjustments by employee
Processing by employee
Contextual Summarizations
Transaction types
Time Trending
Month, week, and day / Also by department
Last month to first 11 months
Transactions at the end of and start of a fiscal year
20. 3/11/2014
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Polling Question 3
What is not one of the query viewpoints?
A. Who
B. What
C. How
D. When
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Other T&E Reports
Unmatched query of cardholders to an active employee
masterfile
Cards used in multiple states (more than 2) in same day
Cards processing in multiple currencies (more than 2) in
the same day
Identify cards that have not had activity in last six months
Cardholders that have more than one card
Extract any cash back credits processed through the card
Extract declined card transactions and determine if they
are frequent for certain cards
Summary of card usage by merchant to find newly
added merchants and most active
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Vendors on report 1 vs. report 2 of duplicate payments.
Duplicate transactions paid on different checks.
Duplicate transactions with debit amounts in the vendor account
Vendors with a high proportion of round dollar payments.
Invoices that are exactly 10x, 100x or 1000x larger than another
invoice.
Payments to any vendor that exceed the twelve month average
payments to that vendor by a specified percentage (i.e., 200%) o
the standard deviation for that vendor.
Vendors paid with a high proportion of manual checks.
Simple Fraud Vendor Scoring
Analysis – How It Started
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The Sampling “Problem”
Bottom Line Numbers
Modern tests (round numbers, duplicates, missing fields)
identify thousands of ‘suspicious’ transactions, usually about
1 in 5 of all transactions get a ‘red flag’
Historically at least 0.02 – 0.03 % of all transactions have
real problems, such as a recoverable over-payment
So roughly 0.00025 / 0.2 = 0.00125 or 1 in 800 ‘red flags’
lead to a real problem.
Imagine throwing a random dart at 800 balloons
hoping to hit the right one!!!
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Transactional Score Benefits
The best sample items (to meet your attributes) are selected
based on the severity given to each attribute. In other words,
errors, as you define them, can be mathematically calculated.
Instead of selecting samples from reports, transactions that meet
multiple report attributes are selected (kill more birds with one
stone). Therefore a 50 unit sample can efficiently audit:
38 duplicate payments
22 round invoices
18 in sequence invoices
….and they are the best given they are
mathematically the most “severe”.
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Summaries on Various
Perspectives
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Summarize by
dimensions (and sub
dimension) to pinpoint
within the cube the
crossover between the top
scored location, time, and
place of fraud based on
the combined judgmental
and statistical score