using data analytics to find and deter procure to pay fraud

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Copyright © FraudResourceNet LLC

Using Data Analytics to Find and Deter Procure‐to‐Pay

Fraud

March 12, 2014

Special Guest Presenter:Rich Lanza


About Jim Kaplan, MSc, CIA, CFE

• President and Founder of AuditNet®, the global resource for auditors (now available on Apple and Android devices)

• Auditor, Web Site Guru,

• Internet for Auditors Pioneer

• Recipient of the IIA’s 2007 Bradford Cadmus Memorial Award.

• Author of “The Auditor’s Guide to Internet Resources” 2nd Edition

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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.


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

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Disclaimers

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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


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







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



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Vendor Billing Fraud/CorruptionIs #1 or #2 No Matter Where You Go


Top Fraud Schemes By Department

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Primary Weaknesses Leading to the Fraud


1. Whistleblowing hotline

2. Signed code of conduct

3. Train employees

4. Background checks

5. Look for and respond to fraud

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Most companies have these procedures in place but the question is….how effective

are they?

The Top ProceduresPer SAS 99 –Appendix B

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Where Are You Using Data Analytics?

AuditNet – 2012 Data Analysis Software Survey


Polling Question 1

What is not one of the top occurring frauds per the ACFE study?

A. Billing

B. Corruption

C. Overstated Revenue

D. Expense Reimbursements

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Mapping Red Flags to Analytics


Report Brainstorm Tool

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Proactively Detecting Fraud

Using Computer Audit ReportsIIA Research Paper / CPE Course

See the IIA’s website at www.theiia.org

The purpose of this document is to assist auditors, fraud examiners, and management in implementing data analysis routines for improved fraud prevention and detection.

A comprehensive checklist of data analysis reports that are associated

with each occupational fraud category per the Association of Certified Fraud

Examiners’ classification system.


Visualizing the Cost Recovery and Savings Process

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Profit Opportunities OutweighAnalytic Costs

Accounts Payable

Audit Fee Benchmarking

Advertising Agency

Document Fleet

Freight

Health Benefits

Lease

Media

Order to Cash

Proactive Fraud Detection Project Fraud Real Estate Depreciation Sales & Use Tax / VAT / R&D

tax Strategic Sourcing Telecom Travel and Entertainment Utilities


Process to Report Mapping

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Fraud Task to Report Mapping


Fraud Task to Supplier Map

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Polling Question #2

What are example cost recovery areas associated with the P2P cycle?

A. Freight

B. Order to cash

C. Healthcare

D. Accounts Payable


Using an Analytic Process to Detect 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


Analytic Command Center

Analytic Command Center

1. Accounts Payable2. Accounts Receivable3. Financial Statement4.General Ledger5. Inventory6.Payroll7. 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


Clear Data Request

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Fraud Data Considerations for the P2P Cycle

A/P and G/L Review FactorsAccounts that are sole sourcedAccounts that have too many vendorsCategories that map to the “recovery list”Assess to industry cost category benchmarksTop 100 vendorsTrend analysis over time Trend analysis by vendor (scatter graph)

Purchase Order / Price ListMatch to invoice payments to assess price

differencesStrategic sourcing vendor review


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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Query Viewpoints


It’s The Trends….Right?

Trend categories (meals, hotel, airfare, other)

Trend by person and title

Trend departments

Trend vendors

Trend in the type of receipts

Trend under limits (company policy)

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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 scaleMay be easiest to use a stratified score

Decide if unique is weirder than non-unique

Relate this summarized list back to the original


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

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Stratify Data ‐ Results


Is Your Organization Working With Banned Companies?

EPLS is the excluded party list service of the U.S. Government as maintained by the GSA

WWW.EPLS.GOV

Now

WWW.SAM.GOV

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Is Your Organization Working With Terrorists?


Are Your Vendors Real?IRS TIN Matching Program

Validates U.S. Tax Identification NumbersCan submit up to 100,000 TIN submissions at a timeMake sure all punctuation is removedSee http://www.irs.gov/taxpros/ and enter “TIN matching program” in the search box

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Polling Question 3

What is not one of the query viewpoints?A. WhoB. WhatC. HowD. When


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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Daily Transactional Analysis


GeoMapping ‐Map Point

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Charting the Score


Scatter Graph

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Scatter Graph Explanation

1 – high dollar change and low count (outliers)

2 – charges that make sense

3 – changes that don’t make sense

4 – inefficiency that is developing


Dashboarding Graphing

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Polling Question 4

What graph is used to map value to score for easier selections of data subsets?

A. Pie

B. Line

C. Bar

D. Scatter


Fraud Scoring for Maximum Impact

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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 anotherinvoice.

Payments to any vendor that exceed the twelve month average payments to that vendor by a specified percentage (i.e., 200%) othe standard deviation for that vendor.

Vendors paid with a high proportion of manual checks.

Simple Fraud Vendor Scoring Analysis – How It Started


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

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Key Control Reports & Scoring


Combining the ScoresACL Code

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Using Vlookup to Combine Scores

Create a record number

Relate sheets based on VLookup


Polling Question #5

What Excel function is mainly used to organize the scores into a master score?

A. SUMIF()

B. COUNTIF()

C. RAND()

D. VLOOKUP()

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Questions?


Peter Goldmann

FraudResourceNet LLC

800-440-2261

www.fraudresourcenet.com

[email protected]

Jim Kaplan

FraudResourceNet LLC

800-385-1625

www.fraudresourcenet.com

[email protected]

Rich Lanza

Cash Recovery Partners, LLC

Phone: 973-729-3944

[email protected]

Thank You!

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Coming Up

Upcoming March Anti-Fraud Webinar…

"How to Use Data Analytics to Expose Fixed Asset and Inventory Fraudsters”, March 19

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