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.

Description: Economic risk analysis has two dimensions: time and frequency. Asset return varies by time because of economic cycles and economic structural changes. Risk is higher during economic recessions and smaller during economic expansions. In addition, different economic structures may exist at different time scales. Risk measures calculated based on daily, weekly, monthly and yearly historical data can be very different. The appropriate frequency depends on the time horizon of the analysis. Therefore, it is important to measure economic risk at both the time level and the frequency level. However, time series analysis and statistical analysis that have been widely adopted in economic risk analysis focus on only one of the two dimensions. In this paper, wavelet models are used to enhance both time‐invariant analysis and time‐variant analysis. Wavelet models can systematically analyze risk by time and frequency and provide richer information than time series models. Using wavelet models, risk measurement can easily be adjusted based on time horizon in a consistent way. Equity VaR estimation is selected to demonstrate the application of wavelet analysis in risk management.

Description: This article provides an overview of the commonly used structural credit risk modeling approach that is less familiar to the actuarial community.

Description: This article describes an initiative of the Chief Risk Officer Forum to perform a study to benchmark internal models, so as to discuss the admissibility of these models for regulatory purposes in the context of Solvency II. The results of and reactions to that study are discussed.

Description: The importance of exposure modelling from a climate analysis perspective.

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: Commentary on practical observations resulting from constructing and running Economic Capital models. Possible standardization of assumptions and result interpretation are morphing even though use of E.C. models has now been around for years.

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

Description: There will be inflection points in the value of an instrument when interest rates change. These inflection points could materially impact the approximation of value change using ALM metrics. In this part of the seven-part series, I will highlight: • How to identify the inflection points; • the range of rate changes where ALM metrics may be relied upon i.e., the “validity” range, outside of which recalculation of the ALM metrics is required and the potential that one needs to rebalance the portfolio; • a technique to improve on the approximation of value change beyond the validity range; and • a technique to convert non-linear change on the yield curve to be used with duration and convexity.