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ERM
AFIR-ERM: Signature-based Validation of Real-world Economic Scenarios
Motivated by insurance applications, we propose a new approach for the validation of real-world economic scenarios. This approach is based on the statistical test developed by Chevyrev and Oberhauser (2022) and relies on the notions of signature and maximum mean distance. This test allows to check whether two samples of stochastic processes paths come from the same distribution. Our contribution is to apply this test to a variety of stochastic processes exhibiting different pathwise properties (Hölder regularity, autocorrelation, and regime switches) and which are relevant for the modelling of stock prices and stock volatility as well as of inflation in view of actuarial applications.
Speaker: Hervé Andrès
Session Moderator: Łukasz Delong
Speaker: Hervé Andrès
Session Moderator: Łukasz Delong
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ERM
Exploring the Asian Solvency Framework: Japan and Korea
In this concluding session of our three-part series, our speakers will explore recent developments in insurance solvency regulations across Asia, focusing on Japan’s Economic Value-Based Solvency Regulation (ESR) and Korea’s K-ICS. We will examine the design, implementation status, and key challenges of each framework. In Japan, preparations are underway for the implementation of ESR in the fiscal year ending March 2026, with key issues including strengthening governance, managing interest rate risk, and enhancing disclosure practices. In contrast, Korea has already adopted K-ICS, facing practical challenges such as alignment with IFRS 17 and disparities in company readiness. The session will also discuss how both systems align with international capital standards (ICS) and the strategic responses required from insurers. It will provide practical insights for industry professionals navigating this period of regulatory transition.
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AI / Data Science
How gaming technology is revolutionizing actuarial work – An actuarial use of GPU Technology
The same processors that once fueled the Bitcoin gold rush and now train cutting-edge AI like ChatGPT are quietly revolutionizing actuarial science. While cryptocurrencies harnessed GPU power to mine digital coins, and AI uses it to process billions of data points, actuaries are discovering these chips can run complex mortality simulations in minutes instead of days. What began as hardware for gaming graphics has become our most powerful tool - turning overnight batch processes into real-time analytics and making trillion-scenario modeling suddenly practical. This presentation will explore how this transformative technology is reshaping our field, and why the actuaries who master GPU computing will lead the next wave of insurance innovation.
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ERM
Retrospective and Prospective Liabilities, Probabilistic and Stochastic Process in Discrete Time for a policy portfolio (I)
This paper is an extension of the “Insurance Risk with Markov Chain Monte Carlo (MCMC) and Method of Moments (MM)”, the method is applied to a portfolio of insurance policies with guaranteed profitability and surrender option. Retrospective valuation is associated with the debt that is reported to the policyholder by the insurer. On the other hand, the insurer performs the prospective valuation of the cash flow that must be discounted with a yield curve, and which must contain the surrender option by the insured. For discrete-time stochastic scenarios, it is feasible to determine the structure of the probability distribution of future flows, which is not necessarily a normal distribution. It will be demonstrated by MM that for insurance with a surrender option, such as endowment or whole life, the expectation of future flows E(BEL) and its respective future variance V(BEL), can be obtained, both in present value, and as a consequence, calculate the contractual insurance margin (MCS) of the insurer. It will also be shown that by MCMC the convergence of the mean (BEL) and sample variance σ^2 (BEL), the probability distribution of future (MCS) flows will be graphed in 2 and 3 dimensions as a function of time. Likewise, it will be demonstrated that there is no single scalar statistic that, when multiplied by the σ(BEL) standard deviation, results in a standardized risk adjustment.
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AI / Data Science
Artificial intelligence (AI) agents in asset and liability management (ALM) within actuarial fields
This research discusses the design, implementation, and performance of AI agents to meet regulatory standards and industry requirements. AI-enhanced ALM can better manage risks and optimize portfolio performance under various market conditions compared to traditional methods. The integration of AI in ALM processes presents promising opportunities for actuaries to advance their risk management frameworks, providing enhanced value to stakeholders in volatile economic landscapes.