Description: In September 2017, the China Insurance Regulatory Commission (CIRC) issued the “C-ROSS Phase II project construction plan”, planned to be used across three years’ time, by perfecting the regulations, improving the operational mechanism and strengthening the regulatory cooperation.

Description: The following case study walks through the steps of a predictive modeling exercise to illustrate one way analytics can streamline the underwriting paradigm.

Description: 1998 Valuation Actuary Symposium. The panelists discussed the benefits of data integration, data warehousing, what OLifE is now, and what it is projected to be in the future.

Description: How the speed of change is accelerating and how we could react to this as a profession.

Description: View the Avoiding Unfair Bias in Insurance Applications of AI Models

Description: This article summarizes five key lessons we have learned in implementing LDTI and shares our insights on practical ways to address them. This is not an exhaustive list of implementation challenges, but companies that can successfully tackle the issues we describe should have a smoother data and systems implementation transition.

Description: This article describes one of the keys to successful marketing in the current environment - build a relational database that will help the agent communicate with a client in an organized way, so that we can educate, inform, present choices and help these clients navigate their financial future. This article provides the elements of such a database.

Description: This session discusses offering disability insurance over the internet with respect to product design, marketing opportunities and technology issues. From the Record of the Society of Actuaries, Volume 27, No. 1, Session 25

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: 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.