Description: This article discusses a good way for insurance companies to calibrate economic capital (tail risk level) that is consistent with the market opinion on its risk. It also establishes a bridge between a risk neutral evaluation and a real world evaluation.

Description: Recent advances in mortality modeling have enabled applicant-specific quantification of mortality risk in life insurance applicants using laboratory and physical measurements. Relative to more conventional underwriting, this approach identifies a small population of significantly under-priced policy and a much larger, cross-subsidizing group whose risk is presently overestimated. Applicants placed in the highest percentile by this scoring methodology are more than 10X as likely to die in a given period as their demographic peers, and represent approximately 5% of short-term claims. Implications for product design are substantial in both the short- and long-term, as the possibilities for adverse selection grow, and the opportunity for fully individualized pricing raises the more distant prospect of products with as many rate classes as applicants.

Description: Findings from EY Insurance Risk Management Team's 2016 (annual) Insurance Chief Risk Officer Survey.

Description: The purpose of this paper is to explore pension plan dynamics— – the behavior of a defined- benefit pension plan over time in an uncertain environment. We proceed by simulating the behavior of a model plan in a stochastically varying economic environment. While both the model and its environment are simplified, the model displays interesting behavior suggesting policy considerations that should be included in the design and funding of these plans.

Description: Increasing uncertainties and volatility in financial as well as real goods markets increasingly reveal that controlling systems based on deterministic and pseudo-exact values are insufficiently eligible to manage the business activities. This raises the question how management systems can be modified to generate the adequate management impulses in volatile periods. Risk management deals from identifying, evaluating and aggregating chances and risks, which again determine the potential fluctuation range of relevant performance indicators. Everyday corporate life shows that risk management is considered important; but the corresponding processes to identify, evaluate and report chances and risks are seldom part of established decision criteria. This “paradox” lets assume that risks are managed in various differing and unsystematic ways. The combination of traditional controlling methods and risk management is not primarily a methodological challenge. The behavior of the whole organization, practiced over a long-year period, has to be transferred into a new corporate culture of transparent dealing with chances and risks. This paper describes the necessary change management process, which has to come along with necessary methodological modifications.

Description: Building on the Part 1 article, Introduction to the Representative Scenarios Method, this article describes testing the methodology with respect to four product types, fixed indexed annuities with GLIBs, level term life insurance, universal life with secondary guarantees, and variable annuities with GLWBs. This Part 2 article describes testing results, refinements to the RSM methodology, and next steps.

Description: View the complete issue for the August 2014 Reinsurance News newsletter.

Description: The purpose of this paper is to explore a methodology for choosing among techniques for managing contribution volatility in defined- benefit pension plans. A frequently used – and as often criticized – method for managing contribution volatility is to base actuarial cost on smoothed assets. Our goal in this paper is to develop a methodology for measuring and evaluating the quantitative impact of competing asset smoothing policies. We proceed by investigating a series of basic pension metrics, evaluating the characteristics of each using a simulated pension plan. We then use normalized and weighted combinations of these metrics to determine an optimal smoothing policy.

Description: We define what we believe a model validation should entail and the value proposition that it offers.