Description: Strategic Risk Management was given lower priority in a recent survey of ERM Objectives. This article tells how the author believes that recent events have caused many to shift to a more short term focus and in the future there will be a shift back to a priority for the longer term view that stands behind Strategic Risk Management when the environment settles down and things again seem to be more predictable.

Description: COVID19 highlights the need for greater complexity expertise in risk management. Crisis like the current COVID19 pandemic and 2008 Financial meltdown and others help us to turn into focus inwards for introspection on how we can better manage our resources and provide a deeper understanding of what our objectives should be and how we can achieve them through applying complexity science.

Description: it discusses the framework and modeling of the market consistent valuation of fixed indexed annuity, against the backdrop of the convergence of accounting standards and internal risk management.

Description: In this article, we summarize inflationary factors that may result in a sharp increase in interest rates and implications for life insurer convexity risk exposure, most notably to dynamic policyholder lapsation. We further provide insights into potential ALM strategies to mitigate this convexity risk. Due to the prolonged low-interest rate environment and low minimum interest rate guarantees offered to policyholders, liability convexity is higher than prior historical periods, which results in life insurers’ ALM positions being extremely vulnerable to short-term interest rate spikes. Convexity mismatches cause issues for life insurers under both rising and declining interest rate scenarios. This means that life insurers are not compensated for taking convexity risk and, therefore, convexity matching should always be considered in the asset optimization process. There are mixed signals from policymakers and financial markets regarding potential inflationary and sharply rising interest rate scenarios. The key takeaway is that life insurers are not compensated for taking convexity risk and it is the right time for insurers to review convexity exposure. There are different approaches available to mitigate convexity exposure through rebalancing of asset positions and hedges within each life insurer’s risk limits.

Description: How insurers can include a liquidity score and liquidity stress scenarios as part of their asset liability management (ALM) protocol.

Description: This paper applies the research in Prospect Theory of Kahneman and Tversky to explain why sales of annuities lag sales of life insurance products. The author theorizes that life insurance is easier to sell because customers perceive themselves to be in the domain of losses, while potential annuity customers perceive themselves to be in the domain of gains. Kahneman and Tversky showed us that people are mostly risk averse in the domain of gains and risk seeking in the domain of losses. Purchase of an annuity or life product is a risky investment. Customers will find it easier to take the risk in the life insurance domain of losses than the annuity domain of gains. The author then proposes a hybrid life annuity product that might address both mortality and longevity risk in one product that would allow both products to be more competitive than stand-alone products.

Description: This article is based on the author's discussions with many executives of insurance companies regarding the challenges and limitations in ERM at its current state of evolvement. The following areas need to be addressed and improved on, in the author's opinion, to 'take ERM to the next level': 1. Reconciling Risk Concerns of Creditors and Shareholders. 2. Managing of Operational Risk. 3. Execution Risks. 4. Producing Credible and Useful Risk Adjusted Performance Measures. 5. Aligning Performance Metrics with Management’s Performance Measurement Philosophy.

Description: Risk management techniques do not include system science. System science requires that the uncertainty of the world is incorporated into the results. The articles explains the difference between risk and uncertainty. Learn how risk practices create an overoptimistic view of future outcomes.

Description: The cyber insurance market looks like it’s growing—rapidly, in fact. However, there’s more to the cyber insurance market than the outward appearance of strong growth. Worldwide aggregate premium’s upward trend may even be cause for alarm. The increase in premium against a flat rate of insured exposure is a key sign that the market could turn from growth to constriction if a solution isn’t found in the near future. If the cyber insurance sector hopes to reach such lofty goals as exceeding $30 billion in global premium by 2028, it’s going to need more capital. Whether directly into the insurance sector or via support from reinsurance and retrocession, fresh capital should help insurers achieve greater market penetration, build a broader foundation for ongoing expansion, and chart a course for long-term sustainable profitable growth. The same sources capital insurers have used since the early days of the market won’t be enough, though. Thankfully, the insurance linked securities (ILS) market seems poised to help, particularly with several promising recent transactions. New sources of capital could bolster the foundation for future cyber re/insurance growth.

Description: This paper evaluates the current practice of operational risk management in the insurance sector. Operational risk is nothing new in insurance, but, because of regulatory requirements, companies have initiated computation of risk capital for their operational losses. The current effort to manage operational risk is not a naturally evolving phenomenon, and operational risk, in the Basel Committee on Banking Supervision’s Basel II definition, is unlikely to be a significant cause of insurers’ failure. In addition, the current Basel II definition of operational risk is not suitable for the insurance sector. Consequently, the invention of models and tools based on the definition is incomplete and illusionary. My findings are based on the analysis of dozens of interviews with insurance industry professionals. I demonstrate the way operational risk is quantified in practice, show that the result obtained from this computation is of littl e use in managerial decision making and propose a set of policy recommendations illustrating the characteristics of operational risk in insurance. This study can be used as a platform for launching dialogues to initiate fresh thinking about operational risk in insurance beyond the current artificial and narrow boundaries.