Why Attend
Processes are becoming more complex; and simple audit techniques and random audit samples are not enough anymore to provide factual evidence to draw conclusions. Given this, it is only through proper data analysis and smart sampling that auditors can perform their responsibilities in an effective and efficient way.
This course develops critical skills in analyzing and interpreting data, which are essential in today's data-driven world. The course equips participants with knowledge and techniques to identify patterns, trends, and relationships in data, enabling them to make strategic decisions that benefit their organizations. Additionally, participants gain skills to confidently design and execute effective audit sampling plans that can greatly improve the efficiency and effectiveness of the audit process.
Course Methodology
Through group exercises, case studies and real-time data bundles, participants will turn raw data into knowledge to understand patterns and identify red flags, or deviations from procedures and standards. Moreover, participants will go through several audit sampling techniques that will help them develop representative and efficient sample sizes that can be applied in their day-to-day missions.
Course Objectives
By the end of the course, participants will be able to:
- Demonstrate a comprehensive understanding of data analysis techniques
- Understand the impact of data analysis on audit sampling
- Use statistical tools to analyze information, manipulate data and conduct audit sampling
- Apply different audit sampling methods to different scenarios
- Identify data anomalies and deviations in audit results to simplify the reporting process
Target Audience
This course is suitable for all Internal Auditors, and those involved in the internal auditing process including, but not limited to, internal controllers, risk officers, external auditors and compliance officers from all levels.
Target Competencies
- Data mining
- Mitigating risks
- Data analysis
- Understanding trends
- Searching for anomalies
