Financial Risks
Liquidity Risk in Insurance: Perspectives from Life and P&C Actuaries
This is a joint webinar between AFIR ERM Section and Enterprise and Financial Risk Forum.
Liquidity risk arises from the inability to meet cash demands (e.g., from claims or policy surrenders) from readily available funds. While liquidity needs are normal for any business, insurers are also exposed to stress liquidity risk, such as from mass policy cancellations and from catastrophes. This webinar focuses on how companies might prepare for and manage liquidity during events that cause significant unexpected liquidity stress.
The session provides insights from both life insurance and property & casualty perspectives.
Speaker: Ralph Blanchard and Tamara Burden
Moderator: Taiga Yokoyama
Liquidity risk arises from the inability to meet cash demands (e.g., from claims or policy surrenders) from readily available funds. While liquidity needs are normal for any business, insurers are also exposed to stress liquidity risk, such as from mass policy cancellations and from catastrophes. This webinar focuses on how companies might prepare for and manage liquidity during events that cause significant unexpected liquidity stress.
The session provides insights from both life insurance and property & casualty perspectives.
Speaker: Ralph Blanchard and Tamara Burden
Moderator: Taiga Yokoyama
Members Only
AI / Data Science
Responsible and Human-Centric AI: Why It Matters for the Actuarial Profession
Amidst the recent flurry of activity around AI, actuaries will be in good company as they consider how to extract the benefits of AI advancements, while avoiding their downside risks. Actuaries that develop and deploy AI systems must consider how to deliver the potential gains in accuracy and speed, while avoiding risks like biased decision-making. At the same time, they must carefully consider how AI systems will integrate into employee workflows. Responsible AI and human-centric AI offer valuable guidance to address these issues and can assist actuaries in realising the benefits of AI.
Speaker: Maura Feddersen
Moderator: Ernst Visser
Speaker: Maura Feddersen
Moderator: Ernst Visser
Members Only
AI / Data Science
GPU’s for Actuarial Modeling, Hype or Hyper-drive?
This presentation will investigate if GPU technology is all hype or if it will be bringing the actuarial modeling space into hyper-drive. Learn about the history of actuarial computing, why GPU’s make sense for actuarial computations, what are the challenges and benefits of GPU’s and if GPU’s truly are the answer, what are the options available for insurers.
Speaker: Martin Brandt, FSA, FCIA
Session Moderator: Ernst Visser
Speaker: Martin Brandt, FSA, FCIA
Session Moderator: Ernst Visser
Members Only
Health
The Impact of COVID-19 and Other Pandemics on the Actuarial Profession
The COVID-19 pandemic, along with other major pandemics such as HIV/AIDS and the 1918 influenza pandemic, has highlighted critical challenges and opportunities that continue to reshape global perspectives and responses. These events have also had notable implications for the actuarial profession, affecting all practice areas. Drawing from its work on pandemic-related issues, the International Actuarial Association (IAA) has identified several key themes that have emerged from examining the effects of pandemics on actuarial practice. To address these, the IAA’s Pandemic Task Force and the Mortality Virtual Forum have produced four key papers:
- Lessons Learned from Pandemics
- Implications of COVID-19 Protection Gaps
- Implications of COVID-19 Data and Pandemic Modeling (to be released in Q1 2025)
- Excess Deaths Analysis: A technical paper reviewing mortality data from 21 countries and exploring various methods for calculating excess deaths.
This session aims to provide an overview of the IAA’s pandemic-related initiatives, foster understanding of the implications for the actuarial profession, and promote dialogue on the lessons learned from these global health crises and how the profession could prepare for the future.
Members Only
Health
Spatio-temporal extensions of the Lee−Carter and Li−Lee models
Several stochastic mortality forecasting techniques, such as the Lee-Carter and Li-Lee models, extract a time-dependent mortality index from logarithmic mortality rates, while producing a residual noise term. These models typically forecast the index while discarding the noise. However, residuals may contain significant temporal, spatial, and spatio-temporal autocorrelation, offering valuable patterns that can be leveraged to improve forecasts. In this study, we propose a machine learning methodology to incorporate this autocorrelation into the mortality index and its forecasts. We introduce two hyperparameters: one regulates the strength of the enhancement, and the other specifies the adjustment method for the mortality index. The adjusted index can then be forecasted using traditional methods such as random walk, ARIMA, and vector autoregression, or more advanced techniques like spatial dynamic panel linear models, eigenvector spatio-temporal filters, and spatio-temporal ARIMA models. Special care is taken to ensure coherence in forecasts across multiple populations, particularly for the Li-Lee model. We apply the proposed methods to data from several countries, testing them on both the Lee-Carter and Li-Lee models. By optimizing the hyperparameters through a train-test-validation split, we compare forecasts with and without incorporating autocorrelational patterns. The results demonstrate that traditional models can be significantly improved by including this additional information. These refined forecasts offer substantial benefits for applications such as life insurance, annuities, pensions, and other areas dealing with longevity risk.