Week 1-2: Introduction to Insurance Risk and Predictive Analytics

1. Insurance Risk Fundamentals:

• Definition and importance in financial services
• Risk categories: pure risk vs. speculative risk
• Insurance risk vs. general financial risk

2. Overview of Predictive Analytics in Insurance:

• Definition and importance of predictive analytics
• Historical development and current trends
• Applications in insurance (pricing, underwriting, claims, fraud detection)

3. Types of Insurance Risks:

• Underwriting risk
• Pricing risk
• Catastrophe risk
• Reserve risk
• Operational risk

4. Predictive Analytics Techniques in Insurance:

• Regression analysis
• Decision trees
• Random forests
• Neural networks
• Time series analysis

5. Key Players in Insurance Risk Management:

• Insurance companies (life, non-life, reinsurance)
• Regulatory bodies (e.g., NAIC in the US, EIOPA in Europe)
• Rating agencies (A.M. Best, Standard & Poor's, Moody's)
• Actuarial consultancies
• InsurTech companies

6. Regulatory Framework:

• Overview of insurance regulation (e.g., Solvency II in Europe, Risk-Based Capital in the US)
• Impact of regulation on risk management practices

7. Data Sources for Insurance Analytics:

• Internal data (claims history, policy information)
• External data (demographic data, weather data, economic indicators)
• Emerging data sources (IoT, telematics, social media)

Ethical Considerations:

• Privacy concerns in data collection and usage
• Fairness in insurance pricing and underwriting

Case Studies:

• Analyze real-world examples of predictive analytics in insurance

Hands-on Exercise:

• Use Excel to perform basic risk calculations and create simple predictive models

Resources:

• "Predictive Modeling Applications in Actuarial Science" by Edward W. Frees
• "Insurance Risk and Ruin" by David C. M. Dickson
• NAIC (National Association of Insurance Commissioners) website for regulatory information
• Society of Actuaries (SOA) website for industry insights and research papers