Description: Read the August 2018 issue of Predictive Analytics and Futurism newsletter published by the Predictive Analytics and Futurism Section.

Description: The advent of machine learning algorithms has taken predictive analytics and risk scoring to a new level. From gradient boosting machines to k-nearest neighbors regression, the use of these techniques has gained significant traction in healthcare - including health systems like the Cleveland Clinic. This session will discuss the use of predictive analytics to go beyond just assessing the risk of a patient, and instead focus on prescribing a specific intervention of care. In practice, the recommendation of a care plan goes beyond just attributing a score to a patient. It provides physicians and care coordinators the most probabilistic options - e.g. referral to social worker, depression screening, weight-loss program - for positively impacting a patient's future healthcare outcome.

Description: Data science and predictive analytics are rapidly growing fields and the tools are evolving at a formidable pace. The actuary who wants to be most effective will most likely have to learn at least the basics of more than one programming language, such as R, Python, or Julia, depending on the problem at hand. The Jupyter Notebook App allows you to create interactive notebook documents that are relatively language agnostic, so you only need to learn a single integrated development environment. More importantly, Jupyter Notebooks allows you to easily combine input (code), output (including visualizations), and documentation in a single, shareable, document. Learn how to make better use of your time with these very useful notebooks. This demonstration-based talk will also cover JupyterLab, the next generation of notebooks.

Description: The Delphi method is a non-quantitative forecasting approach that is most useful when other forecasting techniques appear to have limited value. The Society of Actuaries (SOA) has had some impressive results from Delphi studies (even quoted in the Wall Street Journal for their uncanny accuracy on long-range predictions). Attend this session to learn about this effective, yet underutilized method of leveraging the wisdom of crowds.

Description: Predictive models have disrupted the traditional underwriting process. In this session, we will review a decade in the life underwriting of a disruptor – the confluence of actuaries, underwriters and clinicians. In addition, perspectives of other stakeholders will be explored.

Description: Why Actuaries Should Welcome Automation

Description: The presenters will explore policyholder experience data for long-term products, risks of over-fitting data in developing behavioral models, and ways to mitigate these risks and develop a sustainable and coherent framework to translate complex policyholder behavior experience data to behavioral models using company- and industry-level data. At the conclusion of the session, attendees will be able to: - Identify key drivers of policyholder behavior for long-term product - Understand the risks of over-fitting data and a false sense of precision in developing behavioral mode - Understand techniques that can be used to mitigate these risk

Description: The article recaps existing ideas for assessing credibility and describes questions remaining to be answered.

Description: Predictive models can improve the traditional actuarial assumption-setting process. Data science techniques can provide deeper insights into experience through their ability to identify key drivers, rank importance and account for interactions. The presenters will cover ways in which companies can utilize predictive analytics to set assumptions, including the various methods available and practical approaches for implementation. They will also discuss the advantages and challenges of using predictive models for setting assumptions for life, annuity and disability products.

Description: This practical session would provide a technical overview about GLM techniques and describe how GLM can be applied as a business tool in solving business problems in life insurance. The session would also focus on recent real-world case-studies across different functional areas like pricing, fraud detection, underwriting and product development.