Description: The insurance industry has a lot to learn from academia's missteps. With predictive analytics assuming an ever-expanding role in risk modeling, carriers would be well-served to establish practices that mitigate the creation of faulty models. Hear practical advice on how to mitigate the risks of overfitting, with a focus on how to translate complex policyholder behavior experience data for long-term products into behavioral models using company- and industry-level data.

Description: As any international traveler who has tried to plug in an electronic device knows, the human race is great at devising many different ways to accomplish the same task. Predictive modeling is no different. Data scientists can choose from a wide variety of languages to prepare data, fit models, and verify results. The Rosetta Stone, which showed writing composed in three languages side by side, was instrumental in translating unfamiliar Egyptian hieroglyphics into the more common Greek language. This presentation follows a similar approach by showing the same predictive model implemented in the following languages: R, Python, Spark, MatLab, and SAS. Participants will compare and contrast each of these languages and, by doing so, build a greater understanding of those languages less familiar to them.