Course Description
Data mining and computer assisted audit techniques (CAATS) are now considered by the audit profession as a core skill. The IIA in their Global Technology Audit Guide #13: Fraud Detection and Prevention in an Automated World, declare “Internal auditors require appropriate skills and should use available technological skills to help them maintain a successful fraud management program…all audit professionals – not just IT audit specialists – are expected to be increasingly proficient…”. This seminar addresses these requirements.
In this seminar, we will discuss the critical issues to be considered:
- Myths and realities about using data analytics tools and techniques to detect fraud
- Benefits of using Computer-Assisted Audit Techniques (CAATs) to detect and investigate fraud
- Critical ways CAATs helps to prevent fraud
- Key fraud detection capabilities of CAATs
- How to obtain management buy-in to implement CAATs for fraud detection, investigation and prevention
- How to implement CAATs into the audit process and mistakes to avoid
Learning Objectives
- Understand the necessity for adopting data analysis and CAATs into the fraud-detection/audit process
- Understand the leading data analytic software applications available on the market and decide which is best for your organization
- Determine training needs for incorporating CAATs into your fraud-audit process
- Determine who in your organization is best qualified to spearhead CAATs implementation for fraud detection/investigation/prevention
Course Outline
The Fraud Problem
- Defining the fraud problem
- A statistical overview of the fraud problem
- Who commits fraud
- The Fraud Triangle (Why employees commit fraud)
- Lessons from “successful” fraudsters
New IIA and ISACA Fraud-Detection Standards
- IIA GTAG #13 and GTAG #16
- ISACA “Data Analytics: A Practical Approach”
- AuditNet Survey results on Data Analytics for Fraud Among Auditors
Getting Started with Data Analytics/CAATs
- Step 1: Conducting the Fraud Risk Assessment
- Identifying the fraud universe
- Using red flags to identify fraud risk
- Step 2: Scoping the use of data analytics based on the Fraud Risk Assessment results
- Inventory fraud
- Payroll fraud
- Vendor fraud
- Financial statement fraud
- Step 3: Identifying the Data to be Mined
- Identify investigation objectives
- Feasibility assessment on how to get the data
- Involving data custodians and/or owners Define the required data parameters
- Identify the data fields/files needed
- Step 4: Acquiring the data
- Obstacles
- Working with IT to obtain and “clean” the data
- Step 5: Physically accessing and importing the data
- Risks of using data originals or copies
- Where to import the data
- Avoid corrupting data
Planning the Approach
- Ad hoc testing
- Repetitive testing
- Continuous auditing
- Continuous monitoring
“How To” Demos
- Detect duplicate payment fraud
- Detect payroll (“Ghost” employee) fraud
- Detect P-Card Fraud
Other Data Analysis Tests That Can Be Performed to Discover Red Flags of Fraud
- Using data analysis to Investigate Fraud
- When to investigate; when not
- Who should investigate
- Data analysis techniques for investigating fraud when red flags have been detected
- Keeping the fraud trail “untainted”
Additional Information
Who Should Attend
- Internal and external auditors
- Internal control professionals
Learning Level
Intermediate
Delivery
Group-Live & Group Internet-Based
Field
Auditing
Advanced Preparation
None
Recommended Prerequisites
Basic accounting and audit concepts.
Session Duration
Online: 2 three hour sessions
On Site: 1 day
CPE Credits: 8