Applied Data Analytics in Risk, Control and Audit™

Program Overview

This hands-on program aims to give the participants an understanding on the types of data analytics, its benefits, the various standards of data analytics along with their respective limitations.
Throughout the program, there will be group discussions, case studies and practical analysis as attendees will learn the different types of data analytics modes. Participants will also be given the opportunity to experience “hands on” in building a Machine Learning model of a business process and discover relationships within datasets.
The program contains a very strong focus on hands-on learning. During the two days, students will spend 30-40% of their time performing practical tasks or taking part in role-playing scenarios.
Software and datasets for Standard, Advanced and “State-of-the-Art” analytics will be provided during the course. Attendees can keep those and use them in their work afterwards.


Learning Outcomes

Upon completion of this program, participants will:

  • Understand the types and benefits of data analytics in Risk, Control and Audit
  • Implement and follow a data analytics process
  • Design standard and advanced data analytics using spreadsheets, specialized software and relational databases
  • Understand Continuous Auditing (CA) and Business Intelligence (BI) as part of advanced data analytics
  • Recognise opportunities for Big Data and Artificial Intelligence (AI) in Risk, Control and Audit

Intended Audience

  • Chief Risk Officers
  • Chief Audit Executives
  • External Auditors
  • CFOs
  • Internal audit directors
  • Risk managers
  • Internal auditors
  • Assurance professionals
  • Risk and governance professionals
  • Internal audit managers and supervisors
  • Finance professionals

Program Level and Pre-requisites for Attending

Foundation level, no pre-requisites

Program Duration

2 days

Number of Attendees

Maximum 17 attendees

Training Methodology

A distinguishing feature of this program is its practical aspect. 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.

It is important to note that a specific module of the program is devoted to creating a real data analytics pilot project, which the participants will be able to use as a blueprint in their teams for a real-life data analytics initiative.

At the start of the course, each participant will receive a USB with a fully configured Virtual Machine that will be used for the practical work throughout the course. Participants will be allowed to keep the Virtual Machine after the course and use the software in their work.

Program Outline

Day 1

  • Introduction
  • The Data Analytics Rationale
  • Definition and Benefits of Data Analytics in Risk, Control and Audit (RCA)
  • Organisational Units and Processes Suitable for Data Analytics
  • The Data Analytics Process
  • Types of Data Analytics
  • Group Discussion
  • Standard Data Analytics in RCA
  • Overview
  • Benefits and Limitations
  • Planning Standard Data Analytics Tests
  • Specific Analytical Techniques
  • Examples of Standard Data Analytics
  • Practical Work
  • Advanced Data Analytics in RCA - I
  • Overview
  • Benefits and Disadvantages
  • Planning for Advanced Data Analytics Engagements
  • Case Studies and Practical Examples
  • Practical Work

Day 2

  • Advanced Data Analytics in RCA - II
  • Overview and Characteristics
    • Continuous Auditing (CA)
    • Business Intelligence (BI)
  • Benefits and Disadvantages
  • Planning and Implementation
  • Group Discussion and Demo
  • “State of the Art” Data Analytics in RCA
  • Big Data
    • Group Discussion: How do you understand Big Data
    • Overview, History and Characteristics
    • Big Data Success Stories – Case Studies
    • Big Data Sources
    • Practical Work
  • Artificial Intelligence and Machine Learning
    • Overview
    • Categories and Types of Machine Learning
    • Practical Work
  • Success Factors and Stumbling Blocks
  • Scenario Work: Putting It All Together
  • Program Review, Q&A, Comments

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