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
Course Outline
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