Artificial Intelligence in Risk, Compliance and Audit™

Asia-Pacific (APAC) and Americas

Online Course


14 Aug 2020
14 Aug 2020
8:00 - 12:15 (GMT +8:00)

Program Overview

This program is part of the Practical Analytics in Risk, Compliance and Audit™ series created and delivered by MBS Academy. The program covers state-of-the-art concepts of Analytics, such as Artificial Intelligence/Machine Learning, types and approaches in Machine Learning and specific Machine Learning use cases in audit/risk, that Audit and Risk professionals and teams can apply in their everyday work.

During the program participants will learn what is Machine Learning (ML) and its types: supervised and unsupervised learning. We will also look at various ML approaches, such as neural networks, decision trees, clustering and anomaly detection. Specific module is dedicated to creating ML models by non-technical professionals, such as audit and risk professionals. Participants will perform exercises, with publicly available tools, to apply ML in cybersecurity and project risk scenarios.

The program contains a significant element of gamification and practical work. Every 15-20 mins there is a gamified activity, designed to reinforce the concepts that have been covered and improve retention after completion.

The last part of the program includes exercises on how to apply Machine Learning to identify network intrusion attempts and classify project risk at project’s start. All datasets and step-by-step instructions will be provided at the beginning of the program. Participants will be allowed to keep these and use them as a blueprint for real-life reviews they may be performing.


Learning Outcomes

Upon completion of this program, participants will be able to:

Intended Audience

Program Level and Pre-requisites for Attending

Foundation level, no pre-requisites


Program Duration

Online – 4:15 hrs 

  •  2 x 2hrs + 15 min break

Number of Attendees

Maximum 30 attendees


Training Methodology

A distinguishing feature of our programs is their practical aspect and the gamification of content and delivery. At the end of each module there is a practical exercise designed to reinforce the concepts of the module and to give participants a chance to “get their hands dirty”. The exercises are either scenarios where participants work in groups or individual tasks executed within a specially prepared software environment.

At the same time, gamification is extensively used throughout all of the content. The gamification is done on two levels: full course and individual modules. The full course gamification is based on a gamified storyline across all modules. It increases the levels of engagement, by introducing a mild competitive element, while providing consistent environment for participants.

On the other hand, small gaming activities are present in all modules. Examples include crosswords – based on the content; games like “Hangman” – using terms from the training program, etc. Their purpose is to reinforce learning, while simultaneously applying the principles of Neuropsychology and letting the brain rest every 15-20 mins for a short period of time. The 15-20-minute rests are key to efficient learning, since 20 minutes is the maximum the hippocampus, a part of the brain that takes in learning, can hold.

Program Outline

  • Definition and Major Categories of Artificial Intelligence/Machine Learning (AI/ML)
    • Introduction and definitions – AI, ML, Deep Learning (DL)
    • The relationship between AI, ML and DL
    • Gamified Activity
    • Major categories of AI/ML
    • Gamified Actitivity
  • Types of Artificial Intelligence/Machine Learning
    • Definitions and Examples
    • Gamified Exercise
  • AI/ML in Risk, Compliance and Audit™
    • Use Cases for AI/ML in Risk, Compliance and Audit™
    • Real-life Case Study of AI/ML in Risk, Compliance and Audit™
    • Gamified Activity
  • AI/ML in Risk, Compliance and Audit™ – The Methodology
    • Overview
    • Representing a Business Problem – “It Starts with Pen and Paper”
    • Creating the model – Inputs, Outputs, Data Sources
    • Gamified Activity
  • Scenario Work – Creating an ML model for Project Risk Assessment
  • Practical Work – Investigating Network Penetration Attempts using ML

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