Description: This paper provides a method for estimating long-tailed unpaid losses from paid loss development using trended generalized bondy development. From Actuarial Research Clearing House 1993, VOL. 3.

Description: The method described in this paper is to allocate surplus to each category of business and reserves in proportion to its estimated effect on an insurer's surplus variation. Risk load for each category of business is then selected so that the return on allocated surplus for each category is equal to the overall return on surplus. From Actuarial Research Clearing House 1993, VOL. 3.

Description: This reinsurance refresh session will review different types of reinsurance, main reinsurance regulations and resources, key reinsurance treaty provisions, as well as recent developments in reinsurance. We will also introduce LEARN, an education initiative sponsored by the SOA Reinsurance Section to help improve communication with regulators.

Description: As technology changes the insurance landscape, it is essential that companies adapt quickly while staying focused on customer needs. This presentation will show how Agile and Design Thinking methodologies can help business and technology teams quickly test new ideas and optimize value delivery.

Description: Big data and predictive models have disrupted the life insurance new business and underwriting processes over the past few years. How did we get here? What does the future hold? This session will review the lessons learned from the evolutionary process and discuss what the potential next steps are in this journey. Are real time fully underwritten decisions an achievable goal?

Description: There is no “industry standard” approach to managing the vega (volatility) risk inherent in Variable Annuity Guarantees, Fixed Indexed Annuities and other equity-linked obligations. In this session, we will introduce and analyze a range of reinsurance solutions, including full vega risk retention, full vega risk transfer and more sophisticated or custom structures that split the vega risk between the insurer and the reinsurer in various ways. We will also explore other approaches in bespoke risk coverage and show the impact of different assumptions on a generic “VA Guarantee Cost Estimate” that has been developed by Munich Re.

Description: In this session, a company will share its experience on managing risks underlying the equity-linked insurance guarantees and what lessons were learned over these years as it adapted its strategies to suit the current market environment.

Description: Representing a large indexed annuity writer, the speakers will discuss the growth and evolution of the market for index annuity products. Moreover, the challenges that growth has introduced and how those challenges are being met. While much of the market consists of simple, accumulation-oriented S&P 500 based strategies with 0% return floors, a significant portion of industry sales come from more complex products with highly customized index strategies and other features, such as living benefit guarantees. Attendees will gain a broad understanding of the indexed annuity market, product and features, operational difficulties in risk management arising from product design, and how actuarial and hedge professionals are managing these issues.

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: Often actuarial practitioners are faced with working with data that is less than ideal. The data may be observed with gaps in it, a model may suggest variables that are observed at different frequencies, and sometimes predictive analytic results are very fragile to the inclusion or omission of just a few observations in the sample. Data, particularly big data, are often "messy" and something must be done about it. What is the actuary to do about these very practical matters? These types of questions are what this presentation is intended to address. Concerns on missing data, outliers, nonlinearity, mixed-frequency, cross-correlations are illustrated using real datasets in the Asia-Pacific countries. Some possible solutions to the messy data problems will be discussed.