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cliftonlarsonallen.com

Using Data Analytics as a Management Tool to Identify Organizational RisksSean Walker, Managing PrincipalRyan Merryman, Senior Manager

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Objectives

• Discuss how data analytics can be used to better identify various risks in an organization.

• Encourage you to use technology to protect the organization.

• Demonstrate the power of data analytics using a case study.

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What and Why Data Analytics?

• Data analytics is the process of accessing, normalizing, and modeling data with the intent of discovering useful information– Often consider a forensic tool– Much more can be learned about your organization

• Large organizations such as States process very large amount of data and often in a decentralized manner– Risk of misappropriations– Risk of management override of internal controls– Risk of the unknown

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Misconceptions

• It’s hard.• It’s time consuming.• It’s expensive.• It’s an audit tool.

• Get it done – – Outsource it– Get trained and get coached

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Organizational Studies

• What is normal for my organization?• What is abnormal?• Who is working in the system?• When are they working in the system?• Who is making all those journal entries?• What systems feed information to the financial

system?

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Types of Risks and Areas of Analysis

Accounts Payable

Fictitious vendors

Fictitious, inflated and / or duplicate invoices

Structured payments

Conflicts of interest

Kickbacks

Bid-rigging

Purchase Cards

Duplicate purchasing and reimbursement schemes

Unauthorized and/or improper purchases

Unauthorized users

Unauthorized SIC codes

Payroll

Ghost employees

Improper supplemental payments

Improper bonus or incentive compensation payments

Inflated salaries

Inflated hours

Travel and Entertainment

Expense

False or inflated reimbursement submissions

Improper use of corporate credit card

Purchase for personal use

Duplicate purchasing and reimbursement schemes

Foreign Corrupt Practices Act

Journal Entries

Unbalanced journal entriesImproper management override

Improper expense capitalization

Improper revenue recognition

Entries to unusual or seldom used accounts

Improper or unauthorized user activity

Entries during non-business hours

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Types of Risks and Areas of Analysis

Accounts Receivable

Fictitious customers

Lapping

Credit balance fraud

Offsets with unauthorized or improper expenses

Improper AR aging

Inventory

Fictitious, inflated, duplicate or unnecessary purchases

Theft through improper write-off

Excessive shrinkage

Revenue

False or inflated sales

Fictitious customers

Improper commission or bonus payments

Revenue recognition abuses including channel stuffing, liberal return policies or bill and hold schemes

Non-Financial

Weblog analysis

Building access logs

Computer print reports

Client proprietary database analysis

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State Specific Risks

• Internal control overrides– Avoid purchase authority escalation– Journal entries

• General disbursements• Payroll• Derived revenues (sales tax, etc.)• Beneficiary payments

– Health and Human Service• Grant payments

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State Pension Plan

Brief Case Study

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Cash Disbursement Testing: Objective - Test 3.1 Million Payments Totaling $7.0 Billion

• Challenges faced by us: Each monthly report provided by the State: – Was in a massive .pdf “print report”

format, each monthly files was approximately 15,000 pages

– There were 3.1 Million payments in FY 2013

– The files were too large to print and so large they crippled laptops

123 ft360,000 pages

6 ft

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Cash Disbursement Testing: The Monthly Report was Over 15,000 Pages

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Time and Effort

Obtaining DataNormalizing DataAnalyizing Data

Most Systems Provide Data in a Usable Form

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Cash Disbursement Testing: Understanding the Data and Year over Year Comparisons• We were able to analyze the files and compare

activity from FY 2013 to FY 2012

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FY 2013

FY 2012

Benford’s Analysis

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FY 2013

FY 2012

Stratification by Payment Amount

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Cash Disbursement Testing: Abnormal Payments• Specific Analysis that would be difficult/impossible

without Forensic Data Analysis:– Payees whose payments amounts varied significantly

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Cash Disbursement Testing: Specific Analytics • Specific Analysis that would be difficult/impossible without

Forensic Data Analysis:– Retirement Numbers that had more than one name associated

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Cash Disbursement –Data Analysis vs. Traditional Procedures

Data Analysis• Imported 100% of data• Reconciled totals to F/S for Completeness Testing• Summary Results that tie to F/S balances and compared

to PY• Run specific queries from which to make Risk Based

selections for test work• More efficient - Analysis FY 2013 procedure took only

about 60% of the time of FY 2012

Traditional Procedure• Random Sampling• No Completeness Testing• Less efficient

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Consider

• How much data is collected in your organization?• How quickly can you analyze the data for

management decisions and internal risks?• As Comptroller’s do you believe you have your hands

around all the state’s transactions?

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Sean M. Walker, CPA, CGFM, CGMSManaging PrincipalState and Local Government414-721-7521

Ryan Merryman, CPA/CFF/CITP, CFESenior ManagerForensic Services310-292-9867