Description: Certain valuation assumptions are difficult to set with a high degree of accuracy, whether because the underlying experience is in different economic or interest rate environments, in addition to other reasons. Examples include assumptions for long-term care insurance, annuity options, lapsation and premium persistency on ULSG. This session will share how the actuary can use predictive analytics to augment the assumption-setting process. Some of the benefits to the actuary (along with the company and the regulator) include more accurate assumptions with lower margins for adverse deviations. Of possible interest as well is that applications go beyond valuation and include acquisitions, reinsurance and pricing.

Description: Baseline and what-if/stressed projections of cash flows, financial statements and capital requirements are critical inputs to a range of business purposes, including forecasts, risk management, liquidity management, strategic decision making, etc. Forward looking projections have historically been inefficient and performed in silos making it difficult to scale the process as CFOs and CROs ask for more. In this session we discuss challenges of the forecast process and opportunities to meet desired future capabilities. We will also discuss “what-if” analysis and ways to support production of forward looking projections for “what-if” analysis in an efficient way.

Description: This session will cover lessons learned during the course of implementing PBR by the panelists. It will focus on areas that were difficult to interpret, unexpected issues and managing the process. It will focus on potential stumbling blocks from an implementation, pricing and product development standpoint. There will also be an overview of YRT treatment to date and an update on any emerging best practices.

Description: Regulatory change and increased focus on internal risk management are driving a renewed interest in model efficiency. In this session, we will review several new and existing tools, that show significant promise for advanced modeling of equity linked products. These include: • Clustering • Use of unique--and potentially a different number of--scenarios for each policy • Semi Monte Carlo (SMC) – this is a proprietary technique that is similar to a combination of Monte Carlo and numeric integration • Using a neural network approach to optimize some of the more complex and manual aspects of clustering and improve the fit of the clustered model • Combining SMC with the use of unique scenarios per policy

Description: Investment risk management has become a core competency of insurance companies, and much has been discussed on this topic. This session examines the current investment risk management practices such as product line specific hedging and macro hedging, with a focus on what still remains to be addressed—whether the tradeoff of addition risk management is worthwhile.

Description: Insurance is a technically demanding, risk-based, capital intensive business. It is therefore not surprising that the industry is fertile ground for the development of technical models used to measure and price risk and manage capital. This session presents the forces which have significantly influenced the evolution of risk and capital management techniques and organization in the insurance industry, including regulatory changes, financial market instability, enlightened self-interest and ultimately a focus on shareholder value creation. Combining over 20 years in the industry in risk and finance roles with a rigorous academic approach, the presenter will provide insights into the most important business questions which insurers face today and the technical tools and frameworks used to answer them.

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.

Description: Discusses four areas of interest: Alternative Examination References, Validation by Educational Experience, Academic Program Accreditation, and an Alternate Route.

Description: Presentation on estimating rates of early-onset Alzheimer's Disease. This document contains incident rates, critical illnese insurance information, adverse selection, and more.