Description: Predictive modeling is a powerful toolset for assumption setting (mortality, lapse, etc.). The multivariate approach has its advantages in handling high-dimensional data, address correlations, and statistical significance. However, building insurance assumptions is far more than a simple modeling practice. One must take care to address how the model is applied and its business implications. This session will share the learnings from multiple projects.

Description: Simple mathematical equations often outperform very complex ones in predictive analytics (PA) situations. This session will cover "why" and "how" to guard against overfitting and other potential pitfalls that might not be intuitive.