Description: A critical step in valuing a company for the purpose of financial reporting is the computation of the market value of economic surplus (MVES). This article provides a novel approach to the valuation of MVES that is stable and reasonably immune to “market noise.” Current approaches to the calculation of economic surplus of an insurance company generally define such surplus as the market value of the assets (MVA) supporting the liabilities less the market value of liabilities (MVL). While MVA is observable in the market, MVL is typically computed directly, without regard to the underlying supporting assets. Subtracting MVL from any value of assets that changes with market movements results in an unstable surplus. Consequently, a surplus that is not fully reflective of market sentiment has been rejected by many. This same approach is further used in the calculation of market consistent embedded value (MCEV) when a balance sheet approach is used .

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

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: This research report that provides a literature survey of methodologies applying machine learning to insurance claim modeling.

Description: Wavelet models are used to analyze risk by time and frequency and provide richer information than time series models.

Description: Many ERM programs fail to include an active step of actual risk treatment and are therefore rightfully characterized as Entertainment Systems. Real ERM will use risk assessment to drive decisions about risk acceptance and risk mitigation. Entertainment systems use risk assessment to create reports and to hold discussions. No actions are taken often because ERM is not empowered to do anything.

Description: Note from Editor introducing the articles in this issue.

Description: This article provides a very high level overview of accelerated underwriting programs and their history in the US marketplace. It then discusses setting up an actuarial control cycle for such products and some items related to risk management that are helpful in this setting. Pros and cons of retrospective studies, random holdouts, targeted holdouts and post issue analysis are discussed. A mathematical example of how to estimate the mortality impact of such programs from early emerging experience is provided.

Description: This report examines the use of a copula approach to build a predictive model to estimate the amount of economic capital a life insurance company needs to protect itself against an adverse movement in interest rates, mortality, and other risk drivers.