Data Analytics for Risk Management

1.1 Understanding risk data sources and structures
1.2 Risk registers, incident logs, and loss event data
1.3 Data quality, consistency, and validation for risk analysis
1.4 Preparing and structuring risk datasets for analytics
1.5 Defining risk attributes, categories, and scoring models

2.1 Analyzing historical risk events and trends
2.2 Frequency, impact, and severity analysis
2.3 Root cause analysis using data
2.4 Identifying risk hotspots and high-exposure areas
2.5 Communicating insights through descriptive risk reports

3.1 Introduction to predictive analytics in risk management
3.2 Risk indicators and early warning signals
3.3 Probability and impact forecasting techniques
3.4 Scenario analysis and risk simulations
3.5 Using predictive insights for proactive risk mitigation

4.1 Designing risk dashboards for different stakeholders
4.2 Risk heat maps and exposure matrices
4.3 Trend analysis and time-based risk monitoring
4.4 Building management and executive-level risk views
4.5 Best practices for clear and actionable risk reporting

5.1 Linking risks to controls and mitigation actions
5.2 Measuring control effectiveness using data
5.3 Monitoring residual risk and mitigation progress
5.4 Continuous risk monitoring frameworks
5.5 Supporting governance reviews with analytics

6.1 Financial, operational, project, and compliance risk scenarios
6.2 Selecting a real-world risk dataset
6.3 Building an end-to-end risk analytics dashboard
6.4 Presenting risk insights to management and stakeholders
6.5 Certification upon successful completion