Learning Objectives
After completing this ACL data analytics training, participants will be able to:
- Design and plan a structured data analytics process for audit and business
- Apply the data analysis lifecycle (Planning → Execution → Reporting)
- Identify anomalies, irregularities, and potential fraud indicators
- Perform advanced data integrity testing
- Generate data-driven insights for audit, compliance, and business improvement
- Improve audit quality using ACL analytics techniques and best practices
Duration: 2 working days
Key Topics
This ACL training course focuses on the practical application of data analytics in real business scenarios, enabling participants to detect risks, anomalies, and fraud more effectively.
Data Analysis Cycle (PIPAR)
Understand the complete data analytics lifecycle, from planning to reporting, using a structured and repeatable approach aligned with audit methodologies.
Advanced Data Preparation
Learn advanced techniques in data cleansing, transformation, and preparation to handle complex datasets and ensure accurate analysis using ACL Analytics.
Anomaly & Pattern Detection
Identify unusual patterns, outliers, and hidden risks using data analytics techniques for audit and fraud detection.
Fraud Indicators Identification
Detect potential fraud through transaction analysis, red flag identification, and risk-based testing using ACL.
Case-Based Analytics
Apply ACL data analytics techniques to real-world audit and business cases to enhance analytical thinking and problem-solving skills.
Sample Case Studies:
- Sales Commission Analysis
- Accounts Payable (AP) Analysis
- Procurement Card (P-Card) Monitoring
- Travel & Expense (T&E) Analysis