Forensic Data Analytics in the Payroll Environment. 6 September 2012

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Abel Ford
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1 Forensic Data Analytics in the Payroll Environment 6 September 2012

2 Disclaimers The content of this presentation is for the purposes of providing the with information on forensic data analytics specific to the payroll environment and is strictly confidential It may not be distributed No part may be quoted, referred to or disclosed in whole or in part to any other party, or be used for any other purpose, without our prior written consent We do not accept responsibility for any loss or damage any third party may suffer as a result of utilising the information provided, resulting from your unauthorised disclosure Page2

3 Contents Background Perspective of payroll fraud and reliance on IT controls How does Forensic Data Analytics fit into Fraud Risk Management? Approach to Forensic Data Analytics Typical areas of focus Payroll Employee expenses and petrol card expenses Questions Contact Details Page3

4 Background Page4

5 What is Fraud? The Association for Certified Fraud Examiners ( ACFE ) defines fraud as any intentional or deliberate act to deprive another of property or money by guile, deception or other unfair means Page5

6 Payroll fraud Enablers, drivers and actions Main enablers of payroll fraud Adding on and paying of Ghost employees Unauthorised payments, including salary increases, bonuses, overtime, leave payouts, etc Duplicate employee records Commission, overtime and leave manipulation Overstating hours worked Main drivers of payroll fraud threats Insufficient processes, policies, procedures and/or controls Lack of segregation of duties Ineffective verification and reconciliation Key actions to mitigate payroll fraud risk Assess, improve and monitor internal controls Take precautions and improve procedures, including limit access to payroll information, strong passwords, segregation of duties, conduct reference / background checks, reconciliations and verification of employees Implementing fraud risk management strategy Page6

7 Perspective of reliance on IT Controls to detect fraud Source: ACFE 2012 Report to the Nation On Occupational Fraud Source: ACFE 2012 Report to the Nation On Occupational Fraud Page7

8 Improve Monitor Forensic Review Strategy How does Forensic Data Analytics fit into Fraud Risk Management? Assess Strategic Tactical Operational Code of Ethics Fraud Risk Assessment Other Policies and Standard Operating Procedures Fraud Related Policies Fraud Awareness Training Surprise Reviews Fraud Response Plan Legislative Compliance Whistleblower Mechanism Fraud Resistance Review Staff Vetting and Life Style Reviews Relationships between Stakeholders (internal and external) Information Repositories Walkthrough and Gap Analysis Forensic Data Analytics System Assurance and Security Third Party Compliance Physical and Information Security People Process Systems Supported by proven investigation methodology Databases Fraud investigation team Page8

9 Approach Forensic Data Analytics Page9

10 Approach Forensic Data Analytics Information And many more Page10

11 Approach Forensic Data Analytics Information (cont d) Employee Master Employee Master Bank EFT Transactions Leave and Attendance Timesheets, Overtime & Expenses Payroll Transactions Physical Access Logs Payroll System User Access Loans Audit Trail Bonuses Telephone And more Page11

12 Red flags Employees Red flags are early warning signs that may constitute irregularities, anomalies, misconduct, waste, abuse and even fraud or corruption. However, there may be valid explanations Red flags include Employee not taking leave or refusing promotion Unexplained and/or unnecessary overtime, especially over weekends. Work very early in the mornings or late at night Unreliable communication and reports Living beyond means (life style reviews) Excessive gambling, alcohol / drug problems Page12

13 Typical areas of focus Page13

14 Typical areas of focus Payroll Employee master Payroll transactions incl. EFT Leave and attendance Timesheet, overtime and expenses Bonuses Loans Payroll system user access Physical access logs Telephone Employee expenses and petrol card expenses Page14

15 Payroll Page15

16 Payroll Red Flags Creation and payment of Ghost employees Unauthorised changes to payroll transactions Unexpected employee increases Duplicate payments Abuse of leave or leave payouts Unauthorised / excessive overtime Excessive / unauthorised bonuses Collecting UIF and Union and not paying over Abuse of loans and advances Terminated employees on payroll Unauthorised / excessive absenteeism Poor administration of information, e.g. duplication of employees (permanent and casual) Non adherence to policies and procedures Lack of segregation of duties Lack of security of building / offices / computers (cyber fraud) Inadequate passwords / sharing of passwords Page16

17 Payroll Red flags ascertained via Forensic Data Analytics Employees with payroll transactions and no leave Employees with payroll transactions that are not included in the employee master Employees with payroll transactions after termination Employees with payroll transactions prior to their engagement date Employees with limited payroll transactions, e.g. for less than 3 months Employees with net pay and limited associated wages, especially when compared to other employees Employees with unexpected increases (period and percentage) Employees with net pay with round amounts or recurring digits Employees with details that match / are similar to other employee(s), e.g. bank account, address, telephone number, ID number, employee number, tax number Employees with multiple employee numbers (based on their surname and name) Employees with limited / missing information Employees with invalid ID numbers Irregular changes identified in the vendor change log, especially on bank account changes and changes in employee status (e.g. inactive - active - inactive) Changes applied over weekends, public holidays or after hours Page17

18 Payroll Example of data standardisation and normalisation Page18

19 Payroll Example of data standardisation and normalisation (cont d) Page19

20 Payroll Example of data standardisation and normalisation (cont d) Page20

21 Payroll Example of results Page21

22 Payroll Example of results (cont d) Page22

23 Employee expenses and petrol card expenses Page23

24 Employee expenses and petrol card expenses Red Flags Employee expenses Employees that approve their own expenses Employees submitting expense claims while on leave Employees submitting duplicate expense claims Employees with unauthorised expenses Petrol card transactions \ Employees with excessive petrol card transactions in terms of total value or volume Employees with unexpected relationships with specific service providers Duplicate petrol card transactions Petrol card transactions not related to fuel Employees with excessive petrol card transactions not related to fuel Employees that purchase unauthorised goods and services with their petrol cards Page24

25 Employee expenses and petrol card expenses Red flags ascertained via Forensic Data Analytics Employee expenses Identify instances where employees approve their own expenses Identify instances where the expense claim dates agree to the employee s leave dates Duplicate expenses Employees with high total volume and/or value expenses Text mining of the expense details to identify unusual terms used to describe potentially improper expenses Petrol card transactions Stratify transactions per card and per service provider to identify potential irregular volumes and values and potential unexpected relationships Duplicate petrol card transactions Petrol card transactions not related to fuel High value transactions, individually and per period High-volume / low-value transactions Unexpected types of transactions, e.g. cash withdrawals or transactions not related to maintenance and tyres Petrol card transactions related to fuel High volume and/or value transactions per filling station and per card, individually and per period Outliers on fuel transactions per card Page25

26 Questions Page26

27 Contact details Susan Breytenbach Director Direct: Mobile: Salome Coetsee Senior Manager Direct: Mobile:

Using Data Analytics to Detect Fraud

Using Data Analytics to Detect Fraud Data Analysis Tests for Detecting Payroll and Expense Reimbursement Schemes 2018 Association of Certified Fraud Examiners, Inc. Payroll Schemes Fraudsters cause their