Advanced Data Analytics for Risk-Based Auditing & Fraud Detection equips auditors, compliance professionals, and analysts with the skills to leverage data analytics for identifying anomalies, assessing risks, detecting fraudulent activities, and enhancing the effectiveness of audit processes.
Duration 5 days – 35 hrs
Overview
In today’s data-driven environment, internal auditors must go beyond traditional sampling methods and adopt full-population, analytics-driven auditing approaches. This Intermediate to Advanced Data Analytics Training is designed to equip Data Analyst Auditors with the technical competencies to independently extract, transform, analyze, and interpret large and complex datasets to support risk-based auditing, continuous auditing, fraud detection, and control validation.
Participants will gain hands-on experience using SQL for data extraction, Python/R for automation and advanced analytics, Excel for audit analytics, and Power BI for visualization and dashboard reporting. The program emphasizes real-world audit use cases, enabling auditors to detect anomalies, identify control weaknesses, perform full-population testing, and generate actionable insights that strengthen governance, risk management, and compliance frameworks.
Objectives
- Extract and query large datasets using SQL.
- Perform data transformation and cleaning for audit readiness.
- Automate audit procedures using Python or R.
- Conduct advanced Excel-based audit analytics.
- Build data models for audit testing and risk assessment.
- Apply statistical techniques for anomaly detection and fraud analytics.
- Perform full-population testing instead of sampling.
- Identify control gaps and irregular patterns.
- Design audit dashboards using Power BI.
- Generate actionable audit insights that strengthen governance and compliance processes.
Target Audience
- Internal Auditors
- IT Auditors
- Risk Officers
- Compliance Officers
- Fraud Investigators
- Data Analyst Auditors
- Governance, Risk & Compliance (GRC) Professionals
- Audit Managers and Supervisors
Prerequisites
- Basic understanding of auditing principles
- Familiarity with Excel
- Basic knowledge of databases (helpful but not mandatory)
- Basic statistics knowledge (mean, variance, correlation)
Course Outline
Day 1 – Foundations of Data-Driven Auditing & SQL for Auditors
Module 1: Data Analytics in Modern Auditing
- Evolution from traditional audit to data-driven audit
- Risk-based auditing and continuous auditing
- Audit use cases for analytics
- Full-population testing vs. sampling
Module 2: Data Extraction Using SQL
- Understanding relational databases
- SELECT, WHERE, GROUP BY, HAVING
- Joins and subqueries
- Extracting audit-ready datasets
- Identifying duplicates and anomalies via SQL
- Hands-on: Audit dataset extraction
Day 2 – Data Preparation & Advanced Excel for Audit Analytics
Module 3: Data Cleaning & Transformation
- Data validation techniques
- Handling missing values and inconsistencies
- Structuring large datasets for analysis
- Data reconciliation techniques
Module 4: Advanced Excel for Auditors
- Power Query for data transformation
- Pivot tables for control testing
- Advanced formulas for exception reporting
- Audit sampling simulations
- Conditional anomaly detection
Hands-on Workshop: Building an Excel-based audit analytics model
Day 3 – Automation & Scripting for Audit (Python/R)
Module 5: Introduction to Python (or R) for Audit Analytics
- Data structures
- Importing audit datasets
- Data manipulation using Pandas (Python) or dplyr (R)
Module 6: Automating Audit Procedures
- Script-based testing
- Identifying duplicate payments
- Detecting unusual transactions
- Benford’s Law implementation
- Generating automated exception reports
Hands-on Workshop: Building a Fraud Detection Script
Day 4 – Statistical Analysis & Anomaly Detection
Module 7: Statistical Techniques for Auditors
- Descriptive statistics
- Correlation and trend analysis
- Outlier detection methods
- Z-score and IQR method
- Regression basics for audit testing
Module 8: Fraud Detection & Risk Indicators
- Transaction monitoring logic
- Behavioral red flags
- Continuous auditing analytics
- Pattern recognition techniques
- Control gap identification
Case Study: Fraud analytics scenario simulation
Day 5 – Data Modeling, BI Reporting & Audit Dashboards
Module 9: Data Modeling for Audit Analytics
- Data relationships
- Data model design for control testing
- Creating calculated measures
Module 10: Power BI for Audit Visualization
- Connecting to audit datasets
- Creating KPI dashboards
- Visualizing risk indicators
- Drill-through and dynamic filtering
- Designing executive-ready audit reports
