Learn how AI technologies can improve audit quality by automating analysis, enhancing fraud detection, and increasing efficiency. This one-day course covers key concepts, real-world applications, and important regulatory and ethical considerations. By the end, participants will be prepared to apply AI tools responsibly in modern audit environments.
Prerequisite
Students should have prior experience with auditing processes, including working with structured and unstructured data, conducting risk assessments, and using digital research tools.
What You'll Learn at a Glance
- Understand the core concepts of AI and how they apply to audit activities such as predictive analytics and automation.
- Review past and current regulatory guidance from U.S. agencies and global standards bodies.
- Identify and evaluate ethical risks including bias, transparency, privacy, and accountability in AI-assisted decision-making.
- Examine real-world use cases in audit functions such as fraud detection, anomaly identification, and document analysis.
- Learn how to streamline administrative processes using AI tools in audit workflows.
Course Syllabus
Module 1: Introduction to AI – History, Data Influences, and Technical Progression
- Define Artificial Intelligence and explore its historical development
- Understand the role of structured and unstructured data in AI
- Analyze how data sources like the Surface Web, Deep Web, and Dark Web feed AI systems
- Discuss cloud infrastructure, big data, and the exponential growth of information
Module 2: LLMs, SLMs, and Artificial Intelligence Architecture
- Differentiate between Large Language Models (LLMs) and Small Language Models (SLMs)
- Explore the structure of AI: AI, Machine Learning, and Deep Learning
- Review major AI tools and platforms (ChatGPT, Bard, Claude, Gemini, LLaMA, Copilot)
- Identify AI applications in transportation, healthcare, fraud detection, and creative work
Module 3: AI Products and Searching Techniques
- Compare AI chat interfaces vs. traditional search engines for audit research
- Understand data privacy concerns including PII and PHI exposure in AI usage
- Conduct AI-powered anomaly detection using real audit datasets
- Create audit summaries using AI and assess their risk implications
Module 4: AI Cautions and Ethical Considerations
- Explore ethical challenges such as hallucinations, bias, propaganda, and data misuse
- Examine AI governance frameworks: EO 13859, 13960, 14110, 14141, 14179
- Understand national and international efforts (NIST, UK, EU) for responsible AI use
- Review the NIST AI Risk Management Framework (AI RMF 1.0)
Module 5: Use Cases in AI
- Identify AI applications in document summarization and audit automation
- Explore additional use cases: P-card analysis, geo-mapping, fuzzy matching, vendor data
- Detect AI-generated content in text and images using forensic and analytic techniques
- Discuss the future of AI: AGI, ASI, rogue models, monetization, and workforce impacts
