Dates : 06 - 08 May 2025
The training is 3x1/2 day from 13:30 to 17:00
Precondition: Participation in Part 1 and Part 2 “Effective Use of ChatGPT in Internal Audit”
Place : Online
Language : English
Deadline to register : 25 April 2025
CPE Points : 12
Instructor: Dr. Dominik FoerschlerManaging Director | Senior E
quity PartnerAudit Research Center | ARC-Institute 
BIO Instructor
 
Part 3B: Fit for Audit Data Analytics for Business Auditors using the Power of reasoning AI 
“Elevate Your Skills with Reasoning AI and Statistical Expertise” 
Discover how to transform your audit data analysis by combining statistical principles with the power of reasoning AI. Learn how to organize your thoughts methodically and accurately to guide AI in selecting the right statistical methods, delivering deeper insights and smarter audit outcomes. 
 
Training Description 
Following the success of Parts 1 and 2, Part 3: Fit for Audit Data Analytics for Business Auditors using the Power of AI introduces cutting-edge advancements from OpenAI's latest developments, including the Strawberry Project’s Reasoning AI and OpenAI o1 capabilities. 
In today’s data-driven audit landscape, the ability to effectively leverage data analytics is essential. This course is designed to empower business auditors to leverage the power of reasoning AI by integrating critical statistical thinking into their audit processes. 
Participants will learn fundamental statistical concepts and descriptive analysis techniques, equipping them with the skills to organize their audit analysis methodically and express their thoughts clearly. 
By the end of this course, auditors will understand how to formulate analysis prompts that guide AI systems, helping them select appropriate statistical methods to analyse complex datasets. This training combines statistical know-how and prompt engineering with practical use cases, ensuring participants leave with a hands-on understanding of how to collaborate with AI to enhance their audit insights. By mastering these skills, auditors will be able to unlock more value from data, improve audit efficiency, and drive stronger decision-making across their organizations. 
Learning Objectives: 
Understand fundamental statistical principles necessary for organizing audit data effectively. 
Learn techniques of descriptive analysis and how they apply to audit contexts. 
Master the process of structuring audit thoughts for prompt creation and communication with AI tools. 
Develop the ability to guide AI in selecting the right statistical methods for various audit scenarios. 
Apply reasoning AI to real-world audit cases, improving audit outcomes through smarter analysis. 
Gain skills in prompt engineering to ensure reasoning AI understands the audit context and delivers valuable insights. 
Understand the relationship between data patterns and audit risks, and how AI can be leveraged to monitor these effectively. 
Learn how to improve audit decision-making through accurate, AI-driven data analytics 
 
Audit Training Agenda: 
1. Introduction to Statistical Thinking in Auditing 
Overview of statistical concepts relevant to auditing 
Understanding data types and their audit implications 
Common statistical pitfalls and how to avoid them 
2. Descriptive Analysis Techniques for Auditors
Key methods of descriptive statistics: mean, median, standard deviation 
Visualizing audit data for deeper insights 
Practical applications of descriptive analysis in audits 
3. Structuring Audit Thoughts for AI Analysis 
Organizing audit questions and hypotheses for data analysis 
Creating structured prompts for reasoning AI 
Best practices for guiding AI in data exploration 
4. Reasoning AI and Statistical Method Selection 
How reasoning AI works: selecting the right statistical tools 
Matching audit needs to statistical methods with AI support 
Use cases: AI-driven statistical analysis in audit scenarios 
5. Real-World Applications of AI in Audit Data Analytics 
Case studies: AI-powered reasoning to detect anomalies and perform risk assessments 
Using AI for continuous auditing 
Integrating AI insights into audit reporting and recommendations 
6. Prompt Engineering for AI Collaboration 
Best practices for crafting precise, context-aware AI prompts 
Aligning audit goals with AI capabilities 
Interactive exercises: building prompts for audit scenarios 
7. Improving Audit Decision-Making with AI Insights 
How to translate AI-driven data analysis into actionable audit insights 
Enhancing auditor judgement with data-supported decisions 
Future trends: AI and the evolution of audit data analytics 
 
Precondition: Participation in Part 1 and Part 2 “Effective Use of ChatGPT in Internal Audit”