Description: Artificial intelligence (AI) and Machine Learning (ML) are accelerating us into the fourth industrial revolution. In the next few years technological change and innovation will be rapid. The implications of AI on the actuarial profession are real and game changing. This session will help health actuaries understand what to expect from AI, how to begin using AI, and how AI will dramatically enhance the actuarial profession in the near future.

Description: Investment Correspondent's report for the Opening session of the 2017 Annual Meeting & Exhibit in Boston, MA.

Description: The predictive analytics application growth in the Asia-Pacific region has been gaining momentum.

Description: A review of the Johns Hopkins Data Science Specialization courses with special emphasis on how they may be useful to actuaries.

Description: Technology has streamlined countless processes in our daily lives, and insurance is no stranger to this transformation. Technology can make insurance simpler and more personal for consumers, and can enable actuaries to better analyze unstructured data, make good decisions, create new products, and find channels to enhance profitability, provided they can tame the flood of information pouring in from across the digital ecosystem.

Description: Eager to learn more about what an InsurTech is, how they operate in the insurance ecosystem and how actuaries can participate in this ecosystem? Join actuaries, InsurTechs and participants in the ecosystem to discuss the recent research report on InsurTechs by the SOA. At the conclusion of the session, attendees will be able to: • Know more about InsurTechs and how they operate • Understand how insurers work with InsurTechs • Discover how actuaries can participate in this ecosystem • Learn practical lessons from both InsurTechs and actuaries This session is presented by the Entrepreneurial & Innovation Section.

Description: The famous 80/20 rule in data science states that only 20% of your time working with data is spent driving insightful business analytics. The rest of your time is spent trying to query, clean and organize your data. Even with this knowledge, the majority of actuarial training/focus is on the 20 percent. This session takes a different approach, focusing solely on speeding up your work on the other 80 percent. Practical examples will be part of a code walkthrough in R.

Description: As M2M are exponentially increasing, we will soon see reinventing of whole business models and customer relationships.

Description: Even the best algorithms are only effective if they are trained on appropriate data. This article discusses common pitfalls in creating a training data set.

Description: A look into how machine learning models implement classification and regression at a deeper level. Covers topics such as multi-regression, softmax, and ROC charts.