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
Introduction to data analysis
- Definition and history
- Current technology, the growing availability of data, and increasing challenges
- The impact of vast volumes of data
- Understanding when and how to corroborate data
- Rethinking the value and usage of data
- Getting real value from the data
The different sampling methods
- Sampling and non-sampling risks
- Statistical and non-statistical sampling
- Random sample vs. population census
- Sampling method vs. sample size
- Benefits and risks of sampling techniques such as:
- Random sampling
- Stratified sampling
- Interval sampling
- Subjective sampling
- Block sampling
- Systematic sampling
- Dollar unit sampling
- Stop or go sampling
Comparison and benchmarking
- Practice Advisory 2320
- Institute of Internal Auditors (IIA) recommendations and publications
- Performance benchmarking
- The evolution of big data
- Evaluating the effectiveness of new data analysis techniques
- Peer benchmarking
Data mining vs. audit sampling
- Different data analysis techniques and selecting the sample
- Employing methods for adjusting sampling size
- Simple excel functions and queries to perform analyses
- Incorporating fraud red flags in the audit sampling
- Practice combining results for different scenarios
Identifying data anomalies and deviations
- Fundamental concepts of data anomalies and deviations
- Importance and impact on business operations
- Techniques for identifying, cleaning, and transforming data
- Types such as outliers, missing values, and errors
- How to identify, analyze and highlight them
- Statistical methods
- Visualization techniques to identify and highlight data anomalies and deviation
- Real-world examples and cases in in different industries and business domains
