Want to Find Payroll Anomalies? Using ACL to Conduct a Risk-Based Payroll Audit

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Want to Find Payroll Anomalies? Using ACL to Conduct a Risk-Based Payroll Audit 2005 N.A.L.G.A. Conference Gerald Schaefer, City of Atlanta

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Want to Find Payroll Anomalies? Using ACL to Conduct a Risk-Based Payroll Audit. 2005 N.A.L.G.A. Conference Gerald Schaefer, City of Atlanta. ACL at the City of Atlanta. Gerald Schaefer, Financial Systems Audit Manager. ACL at the City of Atlanta. - PowerPoint PPT Presentation

Transcript of Want to Find Payroll Anomalies? Using ACL to Conduct a Risk-Based Payroll Audit

Page 1: Want to Find Payroll Anomalies? Using ACL to Conduct a Risk-Based Payroll Audit

Want to Find Payroll Anomalies? Using ACL to Conduct aRisk-Based Payroll Audit

2005 N.A.L.G.A. Conference Gerald Schaefer, City of Atlanta

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ACL at the City of Atlanta

Gerald Schaefer, Financial Systems Audit Manager

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ACL at the City of Atlanta

Formerly Internal Audit Division – Dept. of Finance

Now City Auditor’s Office Independent office; established by City CharterReport to audit committeeFirst city auditor hired in October 2001Medium audit shop – 11 professionals; 2 support Inherited former Internal Audit Division staff;

reorganized early 2003 to accomplish new mission

Background

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Payroll Audit: Background

Law and Finance Departments identified theft in pension fund by former employee; we did an audit to identify the magnitude of the fraud

Since fraud was discovered in the pension audit our objectives for the payroll audit were:Are incompatible payroll duties and functions adequately

separated to limit opportunities for fraud and abuse?

Are controls sufficient to prevent fictitious employees from being added to the city’s payroll?

We used ACL for two purposes:To verify the identities of employeesTo identify payroll anomalies

ACL

Old Fashioned Audit Work

BackgroundBackgroundBackground

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Ghost Employees

City ordinance mandated that our office conduct a payout to employees

Problematic since the city has over 8,000 employees spread far and wide

Did a survey on the N.A.L.G.A. listserv regarding experiences with payouts—the overwhelming response was:

We did what any audit shop would do—we stole an idea from another city

We decided to visit employees at their work site unannounced and verify their identity

DON’T DO IT!!!!!

BackgroundBackground

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Employee Verification

We got the following data from our IT department: payroll data; a list of active vendors and addresses; a list of frozen, abolished, and vacant positions

We submitted a list of city employees and their SSN, DOB, and gender to the Social Security Administration

We placed the results from the SSA and the data from IT into ACL and ran twenty different filters (or queries) to identify medium to high risk employees

Employee Verification

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Employee Verification Criteria

Risk Level Queries

High (10 points) SSN not listed, name & SSN mismatch, no withholding, no fed Medicare, no deductions, duplicate accounts, duplicate addresses

Medium-High (5 points) SSN & gender mismatch, SSN & DOB mismatch, duplicate position numbers, duplicate SSNs, vendor address, abolished position

Medium (3 points) PO box and direct deposit, non-Georgia address, city hall addressEmployee VerificationEmployee Verification

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How We Scored Employees in ACL

Created three tables—high, medium high, and medium by using the ACL command of Extract and Append to existing file

The tables had the following results: high (372 records), medium-high (318 records), and medium (146 records)

Created a score field in each table and set the default value to “10” in the high table, “5” in the medium-high table, and “3” in the medium table

Summarized each table by the field SSN and subtotaled the field of score

Summarized the three tables into one table by the full name field and subtotaled the score again

Employee VerificationEmployee Verification

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How We Used ACL

Each filter identified between 1 and 146 employeesUsed the ACL command of Duplicate to identify:Employees that had payments to more than one SSNEmployees that had the same account but different last

namesEmployees that had the same address but different last

names

Any employee with a score of 10 or more points was included in our sample.

At the end of the audit we had 321 separate tables and the ACL project including the underlying data files was 6.1 GB.

Employee VerificationEmployee Verification

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Linked Tables to Pay Locations

Identified high risk employees and requested payroll to provide the work locations of all city employees

Related tables by the field pay location to identify where the high risk employees worked

Had to contact some departments directly to identify locations, but always asked for all of the employees at the location

Employee Verification

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Verifying Employees

This is the form we used to conduct the employee verification

Employee VerificationEmployee Verification

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The Verification Process

Our entire office participated in the verification process

Divided the city into four sections and had a team of two auditors assigned to each section

Did it over two days (Tuesday and Wednesday)

If the person was not there, we left a letter instructing them to come to our office to do the verification

Lengthy process: we started the verification process on 12.8.04 and finished on 1.25.05

For the most part everyone cooperated…

Employee Verification

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What We Found

One employee quit rather than have us verify his SSNWe ran his SSN through some databases and found that

his SSN was being used by several people

We also submitted a list of name mismatches to a company to have the names ran against a list of deceased individualsWe found that one former city employee was using the

SSN of his deceased motherWe also found another former city employee using a

SSN that was used by several peopleWe forwarded these employees to SSA’s Allegation

Management Division Employee VerificationEmployee Verification

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Payroll Anomalies Overview

City uses PeopleSoft 7.62

Scope of our review was January 2002 through July 2004

Over this time frame there were

For paychecks we requested 116 fields and for direct deposits we requested 118 fields

All the fields came from PeopleSoft except for the field: “cashed?” which was a yes or no field

561,404 records

Data Validity

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File Review

Right after we got the data from DIT we randomly selected 30 active employees and 30 terminated employees.

For these employees we pulled their payroll file and their human resources file to verify that the information in PeopleSoft, which was the source of our data, was accurate.

We used ACL to do the sampleSampling—Sample recordsSelect “Random” under Sample ParametersSelect “Record” under sample typeEnter number of sample in “Size”

Data ValidityData Validity

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Sample Results

Data Validity

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Data Validation ACL Command How to Get There

Verify Data—Verify—Verify Fields

Statistics Analyze—Statistical—Statistics—Statistics On..

Classify Analyze—Classify—Classify On…

Count Analyze—Count Records…

ISBLANK( string )

Edit View Filter—Functions

Data Validity

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Data Validation-Verify

Data ValidityData Validity

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Data Validation-Statistics

Data ValidityData Validity

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Using the Fraud Toolkit for ACL Identifying missing checks and direct deposit

advices ACL steps

Select Fraud Toolkit—Right click on the Start script—Select Run—Click on OK

Select Gaps Select input file (can apply a filter)Select maximum number of items to list (We chose 10,000)Select field to test for missing recordsResults appear in ACL log, not as a table

Results We had: 5,454 missing checks and 975 missing

direct deposits Talking to IT we also identified 4,292 manual checks we

did not have

Data ValidityData Validity

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Eye-popping Results Did we have any positions filled by more than one

person?

ACL stepsSelect Fraud Toolkit—Right click on the Start script—Select

Run—Click on OKSelect DuplicatesSelect Input File and Name Output File Select which fields you want to analyze (can choose up to

four)Select a field, which has to be different from the fields

selected in the previous step (optional). We chose employee ID

ResultsWe found 1,200 instances involving 193 employees in 91

positions for 2 or more pay periodsAnomalies

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Using ACL to Identify Payroll Anomalies

Terminated EmployeesAre terminated employees paid after they leave city

employment? ACL steps

Sorted payments in descending order by pay ending date and used the ACL command of summarize on the field of employee ID

Created a unique field in the payments table and in the terminations table. The unique field was SSN + Position number. Related the tables by this field

Created the field of: Days paid after termination: pay ending date – termination date if…termination date > `200020101`

Created the field of: Paid after termination : if…termination date < pay ending date then the value of “Yes”

Created the filter: Paid after termination=“Yes” and Net Pay >0 and Hours >0 and Type2 <> “REG” and Cashed= “Yes”

AnomaliesAnomalies

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Terminated Employees

AnomaliesAnomalies

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Using ACL to Identify Payroll Anomalies

Compensatory TimeWanted to identify if any exempt employees received a

pay-out of unused comp time when they left city employment

The filter we used to identify this was: (Type_of_Pay2 = "CMP" AND Hours2 > 80) OR (Type_of_Pay3 = "CMP" AND Hours3 > 80) OR (Type_of_Pay4 = "CMP" AND Hours4 > 80) OR (Type_of_Pay5 = "CMP" AND Hours5 > 80) OR (Field_50 = "CMP" AND Field_51 > 80) OR (Field_58 = "CMP" AND Field_59 > 80)

The results of this filter were saved as a separate table and an additional filter was created in this new table, which was: FLSA_Status = "X" AND ( Termination_Date >= `20020101` OR Action_Date>= `20020101`)

Anomalies

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Payroll Anomalies

Overpayments

We ran several filters to identify a large number of regular hours reported in one pay period

We found a lot of mistakes, such as vacation pay-outs being listed as regular hours, employees missing their first paycheck then being paid for two pay periods in one pay period, which resulted in pay for 160 hours

We did identify 7 instances where employees were overpaid $33,535.

AnomaliesAnomalies

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Yikes!

$317,534

Anomalies

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What?!

$227,620.14

AnomaliesAnomalies

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413.93

413.93

Background Employee Verification Data Validity Anomalies Lessons LearnedAnomalies

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400 hours

Background Employee Verification Data Validity Lessons LearnedAnomalies

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Payroll Anomalies

Negative leave balancesNegative Balance Number of

EmployeesDollar Amount

Compensatory Time

386 $127,304

Vacation 41 $17,885

Sick 300 $444,673

Reserve Sick 21 $11,392

AnomaliesAnomalies

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Payroll Anomalies

Negative leave balances of employees who were terminatedNegative Balance Number of

EmployeesDollar Amount

Compensatory Time

50 $29,164

Vacation 11 $5,877

Sick 21 $36,570

Reserve Sick 1 $89

AnomaliesAnomalies

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Quick and Dirty Analyses

Identified 13 employees who received 19 payments with the SSN field blank

Used the ACL function of ISBLANK( SSN)

Identified a payment made to the SSN of 999-99-9999 (identified by sorting the field of SSN)

Identified 8 employees with multiple employee IDs by using the ACL function of duplicates on the SSN field after summarizing by the field employee ID

Identified 34 employees with multiple SSNs

AnomaliesAnomalies

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Payroll Anomalies

Uncashed payroll checks

In Georgia, any payroll check uncashed for more than one year must be remitted to the state as unclaimed property

We ran a filter that identified all uncashed checks and used the ACL command of summarize on the field of employee id to identify the number of employees

We then used the command of Analyze—age

We identified:

1,156 checks To 724 employees Totaling $469,071

AnomaliesAnomalies

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Payroll Anomalies

Overtime

We identified 307 employees who were exempt under the FLSA but were paid overtime

We identified 297 nonexempt employees who worked less than 40 hours in a week but were paid overtime

We identified 600 nonexempt employees who worked more than 80 hours in a two week period that were not paid overtime

AnomaliesAnomalies

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Background Employee Verification Data Validity Lessons LearnedAnomalies

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Background Employee Verification Data Validity Lessons LearnedAnomalies

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Background Employee Verification Data Validity Lessons LearnedAnomalies

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Background Employee Verification Data Validity Lessons LearnedAnomalies

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Quality tests

These tests did not produce any findings but have merit nonethelessIdentified employees who received the biggest

increase in salary and reviewed their personnel files to verify that the raises could be documented

Reviewed the supporting documentation for any pay adjustments over $1,000

Reviewed any payroll payment over $10,000Pay-outs of sick leave balancesCheck reversals net to zeroMore than one payment per period to the same

person

AnomaliesAnomalies

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Lessons Learned

Don’t be coy—Ask for everything: more detail=less false positives=less work

Do your homework before you start—identify all ordinances, policies, and practices, then look at the data

Dig deeper—We ran the queries, researched the results in PeopleSoft, pulled personnel files, talked to management

Don’t assume anything is done correctly

Trust your instincts—if something does not seem right it probably is not

Keep your mouth shut until you have all of the facts

Lessons Learned

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Lessons Learned Part Deux

When you do complex filters, know the answer before you run the filter and then review results

Always validate that your filter produced what you thought it did by examining the actual data

Meet with management to discuss results and to identify plausible explanations—the sooner the better

Use common sense

Keep your eyes open

Use ACL Support – there is no shame in asking for help

Lessons LearnedLessons Learned

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Questions

For further information, contact:

Gerald Schaefer [email protected]

404.330.6876

Y’all come down and see us!

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Muchas Gracias!!!

Thanks for

Your Time!

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