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: Often actuarial practitioners are faced with working with data that is less than ideal. The data may be observed with gaps in it, a model may suggest variables that are observed at different frequencies, and sometimes predictive analytic results are very fragile to the inclusion or omission of just a few observations in the sample. Data, particularly big data, are often "messy" and something must be done about it. What is the actuary to do about these very practical matters? These types of questions are what this presentation is intended to address. Concerns on missing data, outliers, nonlinearity, mixed-frequency, cross-correlations are illustrated using real datasets in the Asia-Pacific countries. Some possible solutions to the messy data problems will be discussed.

Description: Often actuarial practitioners are faced with working with data that is less than ideal. The data may be observed with gaps in it, a model may suggest variables that are observed at different frequencies, and sometimes predictive analytic results are very fragile to the inclusion or omission of just a few observations in the sample. Data, particularly big data, are often "messy" and something must be done about it. What is the actuary to do about these very practical matters? These types of questions are what this presentation is intended to address. Concerns on missing data, outliers, nonlinearity, mixed-frequency, cross-correlations are illustrated using real datasets in the Asia-Pacific countries. Some possible solutions to the messy data problems will be discussed.

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: You’ve built or updated a predictive model. Is it any good? How can you interpret and understand its predictions? How does it compare to alternative models? This session will look at traditional and non-traditional ways of validating predictive models, with examples from case studies.

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: This practical session would provide a technical overview about Generalized Linear Model (GLM) and describe how GLM can be applied as a business tool in life insurance. The session would also focus on case-studies sharing across different functional areas such as pricing, fraud detection, underwriting, product development and other real-world applications.

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.

Description: Discussion of machine learning applications currently being used to augment traditional actuarial modeling in the life insurance industry. This includes data science techniques for: Quantifying and explaining differences between model versions or reporting periods Building proxy models for actuarial model output using machine learning One-class classification for data integrity checks Applications of unsupervised learning (e.g., clustering and principal components analysis) to reduce model run time