Description: After International Accounting Standards Board (“IASB”) issued International Financial Reporting Standard No. 17 – Insurance Contracts (“IFRS 17”), IASB received much feedback and suggestions for improvement. As a result, in early 2019, IASB made limited changes to IFRS 17. The presentation will briefly discuss the main contents and effects of those changes.

Description: The session will talk about the basic knowledge of GLM model that is relevant for insurance professionals. It will consist of explaining the concept, issues, tools and applications especially from insurance industry perspective. The session will also talk about how actuaries can benefit from predictive analytics. Advanced business analytics provides employers with insightful decision making and affords the opportunity to assess a marketplace from a totally new perspective. In this session, I will share some modelling techniques used in general insurance company for reference.

Description: This session will discuss strategy and tactics for becoming good stewards of consumer/customer data. Topics will include various techniques, such as anonymization, pseudonymization, tokenization, and the merging of disparate, de-identified data sources.

Description: As any international traveler who has tried to plug in an electronic device knows, the human race is great at devising many different ways to accomplish the same task. Predictive modeling is no different. Data scientists can choose from a wide variety of languages to prepare data, fit models, and verify results. The Rosetta Stone, which showed writing composed in three languages side by side, was instrumental in translating unfamiliar Egyptian hieroglyphics into the more common Greek language. This presentation follows a similar approach by showing the same predictive model implemented in the following languages: R, Python, Spark, MatLab, and SAS. Participants will compare and contrast each of these languages and, by doing so, build a greater understanding of those languages less familiar to them.

Description: Tomorrow’s risk landscape is shaped by macro trends and their interconnections, and those new, changing but not-yet envisioned risks will shape tomorrow's insurance needs. Karen would like to share Swiss Re's insights on new emerging risk themes and trends, climate change and its effects on the L&H business. Hopefully the session can provoke new ideas and enable us to build resilience together.

Description: To discuss actuaries, as a rational group with systematic professional knowledge training in life insurance companies, should participate more in the discussion of enterprise management and make greater contributions to the industry at the stage when the industry is facing transformation and exploring new development ideas.

Description: How to make the third pillar of the pension system develop faster is a hot topic in the field of social security research in recent years. Its development is facing three dilemmas: cognitive dilemma, economic transformation dilemma and incentive dilemma. According to the order from easy to difficult, four suggestions are put forward: to build the third pillar system by introducing personal accounts; to separate individual accounts from urban and rural residence insurance and put them in the third pillar; to integrate housing funds into the third pillar; to turn short-term bank savings into long-term pension reserve by improving social welfare.

Description: Market consistent valuations require path-dependent discounting for scenarios. IFRS 17's requirement to discount best-estimate cashflows using locked-in rates could give non-sensical results in many instances. This article describes a technique for calibrating each of the scenario's cashflows so that they can be discounted with other discount factors or curves without distorting the value of underlying options and guarantees.

Description: LDTI’s introduction has forced many insurers to examine their existing operations and technologies. They must determine how to optimize compliance efforts while balancing data, analytics and system modernization considerations to achieve maximum value. This article will examine the four key facets—the data, technology, processes, and people—essential to any successful LDTI exercise. It will also touch on the various approaches for tackling LDTI and how a modernization mindset can help insurers realize much greater transformation for the business.

Description: This article provides a brief overview of the current state of machine learning (ML) and artificial intelligence (AI) as they apply to actuarial science, including discussion around the integration of actuarial judgment with ML and AI. It concludes with an example of explainable ML through a python tool known as SHAP (SHapley Additive exPlanations), which is able to explain the key drivers behind individual ML or AI predictions.