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: The valuation manual is a living document that continues to evolve. As more companies adopt Principle-Based Reserving (PBR) and file PBR Actuarial Reports, regulators have observed gaps and inconsistencies across the industry. As a result, PBR reporting requirements (VM-31) have expanded significantly in 2020 and still continue to evolve. This session will provide attendees with everything they need to fill out their PBR reports for both Life business and Variable Annuity business. We will dig into the new changes effective for 2020 reporting and approved changes for 2021, and compare and contrast the Life and VA requirements to help companies identify areas that can be leveraged between the two. In addition, we will share insights from regulators, consultants, and company actuaries on what to include or exclude in your report.

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: 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 presentation develops a micro–level stochastic model for the run–off of automobile insurance claims. The analysis uses policy exposure, claims and payments experience data from vehicle insurance portfolios obtained from a major general insurance company in Singapore. The data are from the General Insurance Association 'GIA' of Singapore, an organization comprising most of the general insurers in Singapore. Modelling the run–off of individual Reported But Not Settled [RBNS] claims is the very aim of the work. The presenters focus on the following components of the development process: the time to an event, the conditional event type, the conditional payment type if any, and the conditional severity if any. They distinguish three types of events: settlement without any payments made during development, settlement with a payment and a payment without settlement. Given that a payment is to be made, the conditional payment type model describes the probability that it will be one of three claim types, or any possible combination of them. The conditional severity component describes the claim amount structure according to the combination of types paid. The presenters provide appropriate statistical models for each component and calibrate them to the available data. These building blocks are consequently used to predict the future development of open RBNS claims.

Description: The insurance industry is known to have high operating expenses in the financial services sector. Insurers, investors and regulators are interested in models to understand the behavior of expenses. However, the current practice ignores skewness, occasional negative values as well as their temporal dependence. Addressing these three features, this presentation develops a longitudinal model of insurance company expenses that can be used for prediction, to identify unusual behavior, and to measure firm efficiency. Specifically, it uses a three parameter asymmetric Laplace density for the marginal distribution of insurers’ expenses in each year. Copula functions are employed to accommodate their temporal dependence. As a function of explanatory variables, the location parameter allows the presenters to analyze an insurer’s expenses in light of the firm’s characteristics. Their model can be interpreted as a longitudinal quantile regression.

Description: This presentation introduces the concepts and methods of trend analysis and a developed an Excel Software package for trend analysis.

Description: This 2005 presentation from the 40th Actuarial Research Conference is about equilibrium pricing models and valuations related to hybrid pension plans.

Description: These are the slides that go with the paper 'Mortality Modeling using Projection Pursuit Regression.' In that paper, the authors develop sixteen separate mortality models based on the current SOA mortality data being used for the preferred life study. The models are constructed using projection pursuit regression. The sixteen models arise from all combinations of sex, smoker status, select and ultimate status, and per life vs. per dollar mortality rates. In general the models confirm the following four facts: 1 Mortality rates increase as attained age increases. 2 Mortality rates increase as duration increases. 3 Mortality rates decrease as Face Amount Bands increase. 4. Mortality rates decrease if preferred underwriting is present.

Description: The prediction of adequate claims reserves is a major subject in actuarial practice and science. Due to their simplicity, the chain ladder [CL] and Bornhuetter-Ferguson [BF] methods are the most commonly used claims reserving methods in practice. This presentation develops an estimator for the conditional mean square error of prediction [MSEP] of the ultimate claim in the BF method. This will be done in the framework of generalized linear models [GLM] using the overdispersed Poisson model motivation for the use of CL factor estimates in the estimation of the claims development pattern.