Description: 1992 SOA Annual Meeting, Washington, D.C. The stated purpose of this session was to discuss specifically how a company can manage and improve its financial performance as measured by the statutory financial statement. Eleven strategies to improve performance were addressed.

Description: 1994 SOA Spring Meeting, San Francisco. This session about fluctuation in interest rates covered: 1. The impact of increasing interest rates on liabilities. 2. Whether insurance companies are prepared for movements in interest rates. 3. The effects that rising interest rates have on the asset side of the balance sheet and some of the associated risks. 4. Investment strategy and asset-liability strategy, using the SPDA product as example.

Description: From a session at the annual meeting of the Society of Actuaries held in Chicago, Illinois, October 15-18, 2000 Panelists discuss the changes these companies were making, the experience so far as a public company, and how investors are viewing them.

Description: 2004 SOA Annual Meeting and Exhibit, New York. The panelists discussed methods of assessing catastrophic risk, offered insights into how reinsurers evaluate it, and discussed tools that assist in quantifying and evaluating the risk.

Description: From a session at the 2000 Valuation Actuary Symposium held in Washington DC, September 14-15, 2000 Panelists use recent transactions and examples to demonstrate various operational, financial reporting and strategic aspects of insurance company mergers.

Description: In this editorial, the author draws on the past to briefly address the uncertainties of the future. From The Actuary Vol. 33, No. 8.

Description: Whether focused on mortality, longevity, health or disability risks, biometric uncertainty is the foundation of our work. With a long history of processes and methods for analyzing biometric considerations, we run the risk of becoming stagnant in our thinking about the subject. The presenters will explore how companies can stay ahead of the curve in an environment of ever-changing medical and biometric influencers. They'll dig into cause of death trends, the obesity epidemic, genetic testing, wearables, other wellness tools and more. The presenters will also address the impact of changing risk assessment methods through predictive underwriting. This session is for anyone interested in staying at the forefront of biometric risk.

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: 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