Description

As internal audit functions evolve within the digital enterprise, leveraging data analytics has become essential for improving planning and fieldwork efficiency, risk identification, and overall assurance. This seminar will explore the shift from traditional auditing to a data-driven approach, highlighting key differences, benefits, and challenges. Topics covered include: Traditional vs. data-driven auditing; Why data analytics matters in modern auditing; Overcoming resistance to change; and, Common myths and misconceptions of data analytics.

Learning Objectives

Description

In this practicum, an industry practitioner will provide real-world examples and case studies that show the practical implementation of data-driven auditing. The session will focus on how analytics are applied across all phases of the internal audit process—from planning and fieldwork to reporting and follow-up—as well as across key business areas. Through interactive discussions and audit case studies, and gain insights into successful implementations. This session bridges theory and practice, equipping you with actionable strategies to modernize your audit approach and address common challenges.

Learning Objectives

Description

Auditors must understand where and how data is stored, accessed, and governed to effectively leverage data analytics. This session demystifies the organizational data ecosystem and provides strategies for accessing, evaluating, and leveraging audit-relevant data sources. Topics include: Understanding data warehouses, lakes, and structured vs. unstructured data; Identifying and building relationships with data owners/stewards; Overcoming access and data governance hurdles; and, Making effective data requests.

Learning Objectives

Description

This hands-on session equips internal auditors with the essential skills to align audit objectives with the right data sources, choose from various data access methods, and assess quality of retrieved datasets. We’ll identify key data risks, retrieve relevant data, and apply fundamental cleansing techniques to ensure accurate and reliable data for audit. Topics include: and, Assessing data quality, completeness, and reliability.

Learning Objectives

Description

With numerous possibilities for data analytics in audit, knowing where to start is critical - and not all data analytics opportunities are equal. This session explores structured approaches to brainstorming and prioritizing data analytics opportunities within audit engagements, ensuring auditors focus on the most impactful and feasible projects. Topics covered include: Brainstorming analytics opportunities in audits; Prioritization frameworks for selecting the best analytics use cases; Aligning analytics with risk-based auditing principles, and, Avoiding the mistake of analytics for analytics’ sake.

Learning Objectives

Description

This interactive practicum bridges the gap between audit objectives and data analytics execution. In this session, you’ll learn how to plan a clear and effective analytic approach, refine and prioritize analytics through stakeholder communication, and collaborate with technical experts to enhance your audits with more advanced techniques.

Learning Objectives

Description

You don’t need specialized software to perform effective data analytics—most auditors already have access to capable tools. This session covers fundamental DA techniques and demonstrates how to apply them using commonly available audit tools, ensuring that auditors can integrate analytics with existing resources. Topics include: Overview of analytics tools: Excel, Power BI, SQL, Python, and audit-specific software; Common analytic techniques; and, Matching audit analytics needs with the right tool.

Learning Objectives

Description

In this hands-on session, participants will conduct a mock audit using data analytics techniques covered in the prior session. You will perform tasks including data transformation, aggregation, risk scoring, trend analysis, outlier detection and basic text analysis on unstructured data. This session emphasizes matching audit analytics needs with the right tool, ensuring you build practical skills to support decision-making and enhance audit effectiveness.

Learning Objectives

Description

Data analytics isn’t just about running reports—it’s about following a structured process to ensure accuracy, reliability, and meaningful insights. This session walks auditors through the phases of the audit data analytics lifecycle and how to apply them effectively. Topics include: Key lifecycle steps: acquire, validate, prepare, analyze, report, document; Common data analytic pitfalls and how to avoid them; and, Ensuring audit integrity through structured data analytic workflows.

Learning Objectives

Description

In this practicum, you’ll transition from theory to action by working with a sample audit dataset. Building on the structured approach from the prior session, you will step through the entire data analytics lifecycle—from acquiring and preparing data to analyzing and reporting insights. This session features guided exercises and peer discussions to help you integrate analytics seamlessly into your audit process, ensuring both clarity and compliance with professional practices.

Learning Objectives

Description

Audit reports must be more than numbers—they must tell a compelling story that drives action. This session explores how auditors can use visualization techniques to communicate audit findings effectively and ensure key stakeholders understand critical insights. Topics include: The Principles of data storytelling; Choosing the right visualizations for different audit insights; and, Communicating analytics findings to stakeholders.

Learning Objectives

Description

Building on the principles introduced in the prior session, this practicum puts theory into immediate practice. Participants will work with a provided audit dataset to extract key insights, select appropriate visualizations, and develop a brief narrative that communicates their findings effectively. Through group exercise, you will gain practical experience in assembling your data story, culminating in a short presentation for constructive feedback. This session is designed to reinforce core data storytelling concepts while honing your ability to present audit insights in a clear and impactful manner.

Learning Objectives

Description

Why audit once when you can monitor key risks and controls on a regular basis? This session introduces the concept of continuous auditing and how auditors can move from static, point-in-time reviews to ongoing, frequent risk and control monitoring and testing. Principles of continuous auditing and monitoring; Technologies and processes for continuous analytics; Overcoming challenges in implementing continuous auditing; and, Integrating analytics with GRC platforms.

Learning Objectives

Description

In this practicum, you will apply the continuous auditing concepts from the previous session in a simulated audit environment. Through guided exercises, you’ll identify key risk areas, brainstorm automated analytics ideas, and explore how to integrate GRC platforms to enhance proactive control monitoring. This hands-on session is designed to bridge theory and practice, offering immediate feedback and actionable insights to refine your continuous auditing approaches.

Learning Objectives

Description

AI and automation are changing the landscape of internal auditing. This session explores emerging technologies like generative AI and machine learning, discussing how auditors can leverage them while managing risks. Topics include: Generative AI and its impact on internal audit; Machine learning for risk detection and fraud analytics; and,  Ethical considerations and limitations of AI-driven audits.

Learning Objectives

Description

In this practicum, you’ll put theory into practice by exploring generative AI and automation tools in an audit context. The session includes an introduction to various AI platforms, a guided tour of how AI is embedded in your existing audit software, and interactive exercises focused on effective prompt engineering. Through collaborative, hands-on activities, you’ll develop and test AI-powered solutions to enhance risk detection and streamline audit workflows while considering ethical implications.

Learning Objectives