Agenda

Wednesday, September 19
11:00 p.m. – 11:00 p.m.

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Thursday, September 20
7:00 a.m. – 8:15 a.m.

Panelists will explore the emergence of data analytics within health care. Genetics/Genomics represent a high potential health medical advance. This session will cover how companies apply them to help the insured live longer with less morbidity and mortality, and to help insurers spend less on prescription drugs. Precision medicine can reduce the number of prescriptions necessary for covered participants, providing both better health results for the participant and lower costs for the health insurance provider.

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8:30 a.m. – 9:45 a.m.

This session will cover new data sources such as electronic health records, social media and the Internet of Things (IoT), and how they are dramatically changing the way we get our information for predictive analytics (PA).

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This topic will cover how to integrate traditional actuarial profit models with data science/predictive models, so one could use traditional data science techniques and modeling approaches to evaluating profitability and customer value. This session will cover rethinking actuarial profit models as an end-to-end model in a data oriented world.  

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11:15 a.m. – 12:15 p.m.

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12:30 p.m. – 1:20 p.m.

Simple mathematical equations outperform very complex ones in predictive analytics (PA) situations. This session will cover why and how to guard against that and other items that might not be intuitive.

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The world has witnessed great advances in machine learning (ML)/artificial intelligence (AI) technologies. However, most AI systems are developed for general purposes (e.g. face recognition, language translation, voice recognition etc.). AI designed for insurance is less common. This session will demonstrate an AI system customized specifically for underwriting purposes and how it improves the efficiency of underwriting workflow.

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1:35 p.m. – 2:25 p.m.

This session will cover how with an education background and business experience, an actuary can quickly learn the basics of data science and perform the important modeling work in insurance.

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The language R is terse, powerful, free and able to run on several different machine platforms and operating systems. Fluency in R may soon become a selection factor in getting your next job. This session will demonstrate some of the predictive power you can have beyond spreadsheets. It will cover how to get started in the new world of predictive analytics (PA). This session will be a beginner-level tutorial of how to fit, validate and interpret a logistic regression model with R.

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2:40 p.m. – 3:30 p.m.

This session will discuss how insurers are applying predictive analytics (PA) to improve their business which will include examples from general Insurance such as auto, home and umbrella.

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The cloud inspires awe and fear in equal parts these days; there is a need to learn more, but there’s much information to learn and it may be difficult to know what to learn next. In this session, we’ll carve off a small corner of the cloud and make it your own. Setting up a Data Science Virtual Machine on Digital Ocean will be demonstrated. It’s the fastest way to get a data science development environment setup for your own use. Many of the topics and ideas covered will apply to almost all other cloud offerings. What use cases Digital Ocean is good for and what use cases you should look elsewhere for will also be discussed.

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3:45 p.m. – 4:35 p.m.

It’s time to connect with SOA leaders, members and fellow candidates, and to enjoy some refreshments. Bring your questions and thoughts about the meeting for a chance to connect one-on-one with industry experts. 

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10:00 p.m. – 11:15 a.m.

The growth of huge social networks like Facebook, Twitter and LinkedIn have sparked innovation in graph analytics. Graph analytics is not about data visualization; it is instead about modeling the connections between people. Graph analytics is used to suggest new friends to connect to and what stories you might want to read in these social networks. These same analytic frameworks can be used for novel applications in health care. For example, inferred referral patterns can be used to build a social network graph of health care providers. From there, interesting visualizations and inferences around network leakage or post-acute care can be generated. This session will introduce the concepts behind social network analysis and provide case studies of its application to health care.

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For many, predictive modeling is simply feeding data into a “black box” and getting output. However, understanding the model development process is critical when it comes to validating model results and implementing learnings in a business setting. This session will cover the process of building a simple model then demonstrating key ways actuaries should validate results.

 

Session Coordinator(s)

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