Description: Reunite with members and non-members in the China actuarial community to discuss recent topics and related issues in areas such as macroeconomic sharing, IFRS 17, product strategy, cross-border communication and discussion, and more. Advance your career through learning and networking opportunities, and share your experiences in the finance and insurance industries, as well as other fields related to actuarial science.

Description: We compare two predictive analytics methods - GLM and GBM in terms of predictiveness vs. interpretability.

Description: Analyzing large quantities of raw complex data is often a challenge in the health care industry. During this webcast, there will be a demonstration of how unsupervised machine learning techniques can be applied to such data to help bring useful insights. This will include examples that use unsupervised learning techniques spanning dimensionality reduction, clustering and anomaly detection.

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: Lincoln Financial Group announced the beginning of a two-year data and analytics talent upskilling program with the Society of Actuaries, a global professional organization for actuaries. The SOA will facilitate training for Lincoln employees through a customized multi-phase educational program focusing on big data and predictive analytics.

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: Feature article discussing the ways artificial intelligence will influence the actuarial field.

Description: This session will cover clustering techniques generally (k-means, hierarchical), and how k-means have been used to segment policyholders using third party features such as credit scores to assess customer profitability.

Description: The presenters will share information about: -Insights into causes of death through the use of predictive models, based on a reinsured U.S. life portfolio, using logistic regression models to understand three main causes of death: cancer, heart and external causes; -Opioid abuse is a widespread phenomenon with drug-related mortality rates rising steeply in the U.S. over the past two decades. But what does the most recent data tell U.S. about the outlook for Opioid-related mortality?; -'Deaths of despair' have been publicized as a cause for the poor performance of U.S. mortality in recent years. The presenters will be looking at the different causes under this label, their evolution and explore the drivers behind them.

Description: This session will review limited fluctuation credibility, and how to apply that approach to a GLM. Alternative credibility approaches through the lens of ASOP 25 will also be discussed.