Description: This study examines and describes parameter uncertainty. The paper provides structures to incorporate parameter uncertainty in quantifying risk.

Description: The monograph walks the reader through the hindsight, insight and foresight building process while discussing theoretical and practical issues supporting the building of valid forecasts with provenance for each of these schema examples. That is, the analyst can document the iterative ‘profiling change processes’ mindscape and landscape elements by which and through which the successful forecast is built. This kind of forecast construction ameliorates the ‘black box’ problem of generating pattern output without an understanding of the pattern’s creation or contextual meaning that is often seen when using other methods, particularly machine learning and artificial intelligence. This issue is discussed, referencing appropriate thought leaders.

Description: The Casualty Actuarial Society, Canadian Institute of Actuaries, and the Society of Actuaries Research Institute are pleased to make available results from the 2021 Emerging Risks Survey, the fifteenth in the series. Conducted by Max J. Rudolph of Rudolph Financial Consulting LLC, the survey incorporated a set of Emerging Risks defined by the World Economic Forum as the basis for several of the questions. The survey also included questions related to current risk management topics.

Description: Enterprise risk management (ERM) is an orderly or guided conduct of an enterprise to deal with risks. For insurers, ERM requires full support and commitment of the senior management. ERM in the U.S. and Canada has evolved over the years into a complex ecosystem.

Description: Discover the latest insights on emerging risks in the Indian market with our comprehensive survey report. Uncover key findings on financial volatility, disruptive technology, and climate change, and learn how AI is reshaping risk management. Compare these results with North American trends and explore the optimistic economic outlook for the India market in 2025.

Description: This report captures expert insights from a March 2025 roundtable on how generative AI is shaping actuarial work and insurance industry practices.

Description: There is value in a framework that allows a smooth transition from a qualitative assessment to a quantitative analysis as entities mature in their understanding of climate risk. In this report, a case study on climate change impact on home prices is used to illustrate a Bayesian network iterative approach to building a model for scenario analysis.

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: Because of the growing significance of Enterprise Risk Management, it is critical to consider how ERM responds and relates to circumstances and conditions. In particular, one megatrend currently impacting our world and our understanding of the economy is the emergence of a behavioral economics perspective. This paper describes the nature of behavioral economics, and considers the potential impact of such a perspective on ERM. From Actuarial Research Clearing House, 2012, Vol. 1.

Description: Early detection and assessment of insurer’s insolvency risk are critically important to regulators, insurers, policyholders, and investors for fiduciary assessment of insurer’s financial stability and for protection of potential financial loss associated with insurer’s insolvency. This proposed study aims to develop a market-based insolvency prediction model to detect financially distressed insurers at an early stage with the information content of higher moments from the market.