📊 Predictive Modeling in Insurance
Predictive modeling uses statistical and machine learning techniques to identify patterns, forecast outcomes, and support decisions across underwriting, pricing, claims, and operations.
📘 Why This Matters
Insurers generate vast amounts of data. Predictive models help transform that data into actionable insights, improving risk selection, pricing accuracy, fraud detection, and customer experience.
🧠Common Modeling Techniques
- GLMs — the industry standard for pricing models
- Gradient boosting — powerful for nonlinear relationships
- Random forests — robust to noise and interactions
- Neural networks — used for complex, high-dimensional problems
📌 Applications in Insurance
- Pricing & segmentation
- Fraud detection
- Claims severity modeling
- Customer lifetime value
- Underwriting automation
