Data Analytics, Continuous Auditing and Audit Automation

This course is designed to equip participants with the knowledge and skills needed to leverage data analytics, continuous auditing techniques, and audit automation tools to enhance the efficiency, effectiveness, and scope of audit processes.

By the end of this course, participants will be able to:

• Develop a continuous auditing programme and process
• Secure management buy-and integrate continuous auditing into your organization’s culture
• Understand the impact of group internal audit functions
• Examine existing examples of continuous auditing strategy and best practice

Content

Applying a technology-enabled approach to auditing

• Data analytics overview
• Business case for data analytics and automation
• Pros and cons of common data analysis technologies
• Common data analysis techniques and methods
• Integrating data analytics across the audit process

Data analysis process and analytic development lifecycle

• Define problem, scope and DA objectives
• Locating, understanding and obtaining data
• Validating, transforming and preparing data
• Running modelling procedures, routines and visualizations
• Interpreting and validating results
• Reporting findings and conclusions
• Refining / sustaining analytics for future problems

Implementing sustainable practices

• Making analytics repetitive
• Developing appropriate standards
• Data analytics libraries and audit marts
• Leveraging external data sources
• Risk scoring entities and transactional data
• Dashboards, scorecards and visual analytics
• Spatial relationships and mapping
• Predictive analytics

Evolving to Continuous Auditing (CA)

• Assurance and the multiple lines of defence
• Fundamentals of Continuous Auditing
• Traditional auditing vs continuous auditing
• Business case for Continuous Audit
• Skill and technology requirements
• Changes to data extraction, data analytic logic and technical documentation
• Effect on audit process
• Considerations for reporting and audit documentation
• Best practices and case studies

Developing a sustainable strategy for technology-enabled auditing

• Common implementation hurdles
• Change management: addressing people and process issues
• Technology planning
• Components of an effective data analytics program
• Developing the data analytics strategy
• Implementation planning and program management

For Whom

Chief Audit Executives, Internal Audit Heads, Managers, Supervisors and Lead Auditors from the private, public and not-for-profit sectors or anyone performing data analysis techniques.

Training Methodology

The training methodology combines lectures, discussions, group exercises and illustrations. Participants will gain both theoretical and practical knowledge of the topics. The emphasis is on the practical application of the topics and as a result participant will go back to the workplace with both the ability and the confidence to apply the techniques learned to their duties.