Description: The presenters will highlight differences in mortality that result from socioeconomic issues. Topics will include the differences in cause-specific mortality among different socioeconomic classes, trends in these gaps over time, and recommendations for reducing inequalities in life expectancy in the future.

Description: The investment grade corporate bond market continues to expand in size and is now nearing record levels. The Bloomberg Barclay’s U.S. Corporate Bond Index, representative of the U.S. corporate bond market, now measures over $5 trillion, nearing its all-time high compared to its $2 trillion mark in 2008. The BBB-rated segment of the index has experienced significant growth, and now represents more than 50% of the corporate bond market. Corporate bond market risk has increased beyond what the ratings indicate. Today's investing landscape of relatively low yields, higher corporate leverage and a later-stage credit cycle presents challenges for both insurance and pension portfolios where corporate bonds are a core investment. <br> <br>With representation from a U.S. portfolio manager, Canadian portfolio manager and an investment consultant, attendees will get a full perspective of the North American environment and how to apply this information when making client recommendations.

Description: The session offers high-level initial steps to get started using R, as well as a list of helpful resources for both general data science and actuarial science with R. Attendees will learn about popular R packages to perform practical tasks such as importing, transforming and visualizing data, as well as packages commonly used by actuaries. You will also be introduced to the markdown package for creating reproducible and reusable analyses and workflows. By the end of the session, you will be familiar enough with using R and RStudio to begin incorporating these tools in your day-to-day work as an actuary

Description: Data is an essential component of financial analysis. In this session, the presenters will discuss data governance/management framework and how it can be applied to US GAAP LDTI and VM-51 regulations. This session will also discuss the important relationship between actuaries and IT and how they can work together towards best practices for extracting, managing and using data needed for various actuarial models. Real life case studies relevant for valuation actuaries (e.g. data needs for LDTI and VM-51) will also be presented and examined, highlighting best practices as well as challenges and pitfalls for setting up and governing data processes along with areas for where good communication and strong relationships are important to success.

Description: Basic feature engineering uses input data to create dummy variables, log transforms, and other single-value transformations. This talk builds upon these univariate transformations and presents several strategies for creating complex multivariate features that can enhance your model's lift. Topics such as target encoding, dimension reduction/projection, row/column, and other feature engineering topics will be covered.

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 presentation will outline the different possibilities insurance companies have to hedge their equity risk for the various products.

Description: This session will explore several critical elements in developing a sustainable and coherent framework to translate complex annuity (VA and FIA) policyholder behavior experience data to assumption models using company- and industry-level data, as well as inforce management techniques to handle related risks.

Description: Life Insurers today are collecting huge volumes of text data though heterogeneous sources which if harnessed in a streamlined process can lead to enhanced decision making for pricing, product development, experience studies etc. This session would introduce the technical framework which can be used to derive insights from unstructured data like raw text.

Description: For insurance companies, modeling the future values of assets and their interactions is useful for setting up asset strategies and managing reserves for liabilities. These are the presentation slides for a research paper that evaluates the ability of Wilkie’s stochastic investment models to predict Toronto Stock Exchange [TSX] price index yield. In the Wilkie Model, correlated econometric indices are modeled through a cascade structure.