Description: This article examines the concept of Actuarial Value, especially as it applies to the Medicare plan of benefits. It discusses the cost-sharing responsibility of Medicare beneficiaries and the means by which they mitigate this risk.

Description: Part I of a two-part series concerning the evolution of the individual market. It begins with background, discusses present markets and 2018 regulations.

Description: The paper argues that Risks Evaluation at its core is a psychological process that can lead to the special type of chaos described in modern Chaos Theory. It will argue that quantification and scientific modeling does not exempt risk assessment from the potential for deep biases. Presented at 2010 Enterprise Risk Management Symposium, Society of Actuaries, April 12-15, 2010.

Description: An ongoing discussion of the theoretically correct discount rate for valuing insurance liabilities: whether or not the credit rating of the insurer should affect the discount rate used. See related articles including the article “On the Fair Value of Insurance Liabilities: The Regulator’s Option” in the December, 2005 issue of The Financial Reporter.

Description: ongoing discussion of the proper interest rate to use in discounting liability cash flows to reflect credit risk of the insurer in a fair value accounting system

Description: This is a presentation from 43rd Actuarial Research Conference ARC, Regina, August 14–16, 2008. This talk will present the moment generating function of the discounted compound Poisson aggregate sums when the deflated claims are PH distributed and moment generating function of the discounted compound renewal sums.

Description: A discussion of the use of stochastic modeling in Embedded Value financial reporting

Description: The paper focuses on value creation for European health insurance companies through the practical application of integrated ERM frameworks.

Description: People naturally observe risk in the form of the range of experienced gains and losses which is usually represented in statistical terms by standard deviation. In most cases, there are not enough observed values to reliably discern the true amount of losses that are possible in extreme situations. Statistical techniques that have long been applied to insurance company risks are being used to extrapolate from observed risk to extreme tails of the distribution of gains and losses. These processes are essentially an extrapolation from the “known” risk of volatility near the mean to “unknown” risk of extreme losses. “Fat tails” is a term often applied to risks where the likely losses in rare situations significantly exceeds the predictions of a Gaussian (“thin tailed”) distribution. This paper will look at the relationship between the observed risk and the extreme risk in statistical insurance r isk models and discuss how we might create a discussion among model users about the appropriateness of that relationship for specific types of risks.

Description: Affordable Care Act and Payment Reform