Description: The surge in interest rates over the past few years presents opportunities for life insurers to enhance their Asset and Liability Management (ALM) positions. Learn about a robust liability-driven strategic asset allocation (SAA) framework and implementation considerations, including key objectives and practical considerations for a successful SAA.

Description: This is the disclaimer for the Economic Scenario Generators tool available from the SOA / AAA on the SOA Website.

Description: This paper explores areas where fuzzy logic models may be applied to improve risk assessment and risk decision-making. It discusses the methodology, framework and process of using fuzzy logic systems for risk management. With the help of practical examples, it is hoped that it will encourage wise application of fuzzy logic models to risk modeling.

Description: 2011 SOA Enterprise Risk Management Symposium, Chicago. In this paper, a multivariate quasi-negative binomial distribution is proposed to model frequency dependence among different risk types. The operational risk diversification effect is illustrated through frequency dependency modeled by the bivariate quasi-negative binomial distribution under a framework of Monte Carlo simulation.

Description: 2021 ImpACT Conference - Session 5A: Pandemic Rewind: Risk Management and Scenario Modeling Learnings 10.26.21 Recording

Description: After more than a decade of a prolonged low interest rate environment, long-term Treasury rates have started to increase with higher volatility. While it is generally agreed that a modest increase in interest rates and inflation is beneficial to the life insurance industry, interest rate risk remains a significant challenge for life insurance industry to manage its long duration liabilities, especially under volatile interest rate environments. Learn how to proactively manage interest rate risk and improve capital efficiency through the cash flow based strategic asset allocation (SAA) framework. Discover practical examples of how to combine asset allocation with derivatives to manage tail risk and liability convexity.

Description: This is the first of a two-part article introducing and elaborating an “Adaptor” strategy for ERM. Such a strategy is intended to fulfil two purposes: first, switching among four styles of risk-coping strategies in order to track qualitative changes in a business’s risk environment; and, second, to support the business in navigating with resilience through all the seasons of a business-economic cycle, i.e., of boom, bust, moderate, uncertain. This latter requires careful study of the nature of resilience, the familiar idea of bounce back, and the less familiar idea of bounce forward. Part I of our article is underpinned therefore by a supplementary document (available from https://riskviews.wordpress.com/adaptor-exceptionalism/), which addresses the issue of structural change in a business or economy. It draws heavily on ideas taken from the discipline of control engineering, as well as some from ecology, social anthropology, and economics. The entire two-part article is thus an exercise in cross-disciplinary Systems Thinking.

Description: Sometimes, rare things happen and the least expected occurs. Indeed, some events occur once or twice in a lifetime leaving little room to learn from experiences. In financial markets, extreme events, no matter how rare, could have a profound impact on a company or even the whole country. Insurers are also not free from the impact of catastrophic large-scale extreme events. As a result, with pervasive economic and financial revolutions sweeping our world and potential devastative catastrophes, the increasing interest in tail risk management is fuelled by practical issues, including investment downside risk and insurance catastrophe risk. Thus, developing statistical techniques to model extreme investment and insurance losses is certainly a major task for risk managers. the purpose of our paper is to apply moment methods to estimate the joint events such as concurrent extreme investment loss and insurance loss. A novel aspect of this article is that it takes into account the correlation between different assets and insurance lines of business.

Description: We find the necessary and sufficient conditions for fitting five given moments by a triatomic distribution. We consider three examples drawn from fire large spread, individual life medium spread and group life small spread insurance data, fit them with triatomics, and compute the ruin probabilities using well known formulas for discrete and for combination of exponentials claim amounts. We then compare our approximations with the exact values that appeared in the literature.

Description: Easy access computing power is harnessed to approximate ruin probabilities. The dynamics of the traditional insurance model are stated in the form of period to period constraints. This allows the development of a life-table with the survival probabilities for a given set of parameters. Given the spreadsheet approach of the model, sensitivity analysis to study the impact of different parameter values and loss variable distributions can be easily implemented.