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  2. Artificial Intelligence vs. Machine Learning for Internal Auditors Training Course

Artificial Intelligence vs. Machine Learning for Internal Auditors Training Course ( aivsml | 35 hours )

Prerequisites:

The Course is designed for all the level soft he internal audit department that require looking to the future of the internal auditing processes with in the AI and ML environment.

Overview:

The session will cover different simple concepts around AI and Machine learning that with help the internal auditor understands their role in the pace of the innovation long with some basic guidance in equipping the motion side risk elements arising out of AI and ML implementation in the organization. During the session, practical scenarios will be discussed that will allow the participants to be able to apply basic concepts

Course Outline:

Day 1:

Module 1:

AI- Past, Present and Future

  1. Evolution of Artificial Intelligence
  2. Deep dive into future trends of AI
  3. AI Technologies- Cognitive Services, ML, DL, IoT, 5G, NLP

Module 2:

Global Regulations and Policies- AI

  1. National Strategies on AI
  2. Race to AI Supremacy
  3. AI in Governments

Team Activity: Hands on in-class group activity on the Module 1 & 2

Day 2:

Module 3:

Art of Possible- AI

  1. Hands on Demos of Use Cases and scenarios from the following industries-
  1. Banking
  2. Fintech
  3. Audit Teams

Module 4:

Data, KPIs and Identifying Threats

  1. Types of Data Sources- structured, semi-structured and unstructured
  2. Examples of possible data sources
  3. Explore factors that affect the maintenance of an AI strategy, such as threats and KPIs.

Team Activity: Hands on in-class group activity

Day 3:

Module 5:

Understanding Machine Learning

  • What is Machine Learning?
  • How is machine learning different from AI
  • Defining Big Data
  • Role of Statistics and Data Mining in Machine Learning
  • Approaches to machine learning?

Module 6:

How to use Machine Learning

  • Devising a strategy
  • Understanding basic techniques
  • Applying Machine Learning to Internal Auditor Needs

Day 4:

Module 7:

Challenges and Approaches to Determining an AI Strategy

  1. Paradigms of Ethical use of AI (Governance)
  2. Opportunities with Ethical use of AI
  3. Challenges with Ethical use of AI
    1. Deep fakes
    2. Data Management challenges in AI
  4. Building most suitable AI strategy for an organization based on its strategic objectives

Module 8:

Using machine learning to provide solutions to business problems

  • Auditing around Machine Learning
  • What are the AI/ML Challenges, Opportunities and Risks?
  • Machine learning and controls
  • The role of the internal auditor in an ML /AI environment

Day 5:

Module 9:

Resource Requirements for Adopting an AI Strategy

  1. Determine the resource needed to adopt an AI strategy
  2. Role of Cloud in the implementation of AI Scenarios
    1. Microsoft Azure AI Solutions
    2. AWS AI Solutions
    3. Google Cloud Platform AI Solutions

Demo: Live demonstration of utilizing cloud solutions for AI Scenarios

Module 10:

Future AI Implications

  1. Future Trends
  2. Gartner Top 10 Strategic Technology Trends for 2020 and beyond

Summary & Next Steps