Course description

THE CONFERENCE

The Data Analysis Conference for Audit Professionals is designed to equip auditors and audit-related professionals with the skills, tools, and methodologies required to leverage data analytics for enhanced audit accuracy, risk detection, and evidence-based decision-making.

This event will bring together industry experts, leading practitioners, and technology innovators to explore the latest advancements in data analysis tools, methodologies, and applications within the audit profession.

Throughout the sessions, participants will gain hands-on insights into using data analytics to uncover trends, identify anomalies, assess internal controls, and improve compliance reviews. From predictive modeling and visualization to continuous auditing and fraud detection, the conference provides actionable strategies for integrating data analytics into all phases of the audit cycle.

Participants will engage in interactive sessions, hands-on workshops, and real-world case studies, focusing on key areas such as:

·       Fundamentals of Data Analysis for Audit: Core concepts, techniques, and tools

·       Advanced Analytics Techniques: Predictive modeling, machine learning, and AI in audit

·       Data Visualization and Communication: Effectively presenting audit findings

·       Auditing Emerging Technologies: Blockchain, cloud computing, and cybersecurity

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

Understand the Role of Data Analytics in Modern Auditing

Recognize how data-driven approaches enhance audit quality, efficiency, and insight;

Apply Analytical Tools and Techniques

Gain practical experience with tools such as Excel, ACL, IDEA, Power BI, Python, or other relevant audit analytics software;

Improve Audit Planning and Execution Through Data Insights

Utilize data to develop audit strategies that are risk-focused and evidence-based;

Design and Implement Continuous Auditing Processes

Establish systems for ongoing data monitoring to support real-time assurance and internal control evaluation; and

Strengthen Compliance and Internal Controls

Use data to assess regulatory compliance and recommend control improvements;

WHO SHOULD ATTEND

Public Sector Audit Professionals

·      Officials from audit units in Ministries, Departments, and Agencies (MDAs)

·      State and federal audit service teams

·      Auditors in government-owned enterprises and parastatals

Internal and External Auditors

·      Audit managers, senior auditors, and audit associates

·      Professionals involved in financial, operational, compliance, or forensic audits

Budget and Planning Officers

·      Involved in expenditure monitoring, performance-based budgeting, and fiscal analysis

·      federal and state ministries, departments, and agencies (MDAs)

Procurement Monitoring Units

·      Professionals using audit data to ensure transparency in public procurement

Public Service Commissions and Reform Units

·      Involved in improving audit systems, data governance, and public sector reforms

Conference Content

Module 1: Foundations of Data-Driven Auditing

• Introduction to Data Analytics in Auditing
• Types of Audit Data and their Sources
• Setting Objectives for Analytical Reviews
• The Role of Data Analytics in Risk-Based Auditing

Module 2: Tools and Technologies for Audit Data Analysis

• Overview of Audit Analytics Software (ACL, IDEA, Power BI, Python, Excel)
• Building Dashboards and Reports for Audit Insight
• Introduction to Automation and AI in Auditing
• Case Study: Using Power BI to Track Compliance Issues

Module 3: Analytical Techniques for Audit Risk Assessment

• Trend Analysis, Ratio Analysis, and Benchmarking
• Applying Statistical Sampling and Testing
• Identifying Anomalies and Red Flags through Data
• Predictive Analysis in Internal Audit Planning

Module 4: Forensic Audit Analytics and Fraud Detection

• Using Data to Detect Fraud, Irregularities, and Misstatements
• Visual Analytics for Transaction Tracing
• Linking Analytics to Forensic Evidence Gathering
• Real-World Case Reviews: Fraud Detection Using Audit Analytics

Module 5: Data Ethics, Privacy & Audit Governance

• Legal & Ethical Considerations in Audit Data Handling
• Data Protection Regulations (e.g., NDPR, GDPR) for Auditors
• Establishing an Audit Data Governance Framework
• Mitigating Data Misuse and Ensuring Compliance

Module 6: Building Capacity for Analytical Auditing

• Developing a Data-Driven Audit Culture
• Upskilling Audit Teams for Analytical Capabilities
• Creating a Roadmap for Implementing Audit Data Strategies

Group Activity: Audit Analytics Implementation Plan

What will i learn?

• Understand the Role of Data Analytics in Modern Auditing
• Gain practical experience with tools such as Excel, ACL, IDEA, Power BI, Python, or other relevant audit analytics software
• Establish systems for ongoing data monitoring to support real-time assurance and internal control evaluation
• Use data to assess regulatory compliance and recommend control improvements
• Utilize data to develop audit strategies that are risk-focused and evidence-based

Frequently asked question