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Emerging Topics Community: Comparison of Regulatory Framework for Non Discriminatory AI Usage in Insurance
In this episode, Joe Alaimo and David Schraub dive into the evolving landscape of AI regulations in the insurance industry. They explore key frameworks from the U.S., Canada, the EU, and China, shedding light on the challenges and opportunities that actuaries and insurers face in ensuring non-discriminatory and ethical AI use. -
Quantum Actuarial: Part 1—The Prelude of the Harmonic Oscillator
The quantum harmonic oscillator model, with its rigorous mathematical framework, delineates the statistical probabilities of microscopic particle behavior. When this theory extends into actuarial science, it heralds the potential for interdisciplinary research to achieve effective integration of theoretical innovation and practical application, thereby opening new avenues for research and practice. This study focuses on the innovative connection between the probabilistic properties of the quantum harmonic oscillator and actuarial practice, aiming to dissect how this quantum model reshapes the understanding of actuarial risk assessment and cost distribution. The core of the discussion lies in revealing the intrinsic mechanisms of the quantum harmonic oscillator wave function and its mapping within the actuarial framework, providing an in-depth analysis of innovative perspectives on insurance cost assessment and interest rate patterns. This interdisciplinary research exploration not only tests the theoretical feasibility at the intersection of quantum mechanics and actuarial science but also deeply analyzes the inherent uncertainty in the financial system. With the oscillatory rhythm of the harmonic oscillator, this article anticipates the opening of a new chapter in actuarial science, seeking the actuarial wisdom hidden within the quantum fluctuations. -
What Does the Video Game Industry of the 1990s and Actuarial Software Industry Today Have in Common?
In this article, Igor Nikitin compares the challenges faced by early 1990s video game developers with those now confronting the actuarial modeling software industry. He explains how game developers, struggling with rising costs and technical complexity, adopted game engines. These engines provided essential functionality along with access to the underlying code, giving teams flexibility to innovate without building everything from scratch. The author argues that actuarial software is reaching a similar inflection point. Many firms rely on expensive, rigid tools with limited customization and high vendor dependence. By adopting actuarial platforms that offer source code access, teams can improve model development speed, reduce costs, and apply modern skills more effectively. The article encourages the reader to consider how this shift could enhance operational efficiency, increase team agility, and support long-term innovation. -
Introduction to Bermuda SBA Modeling: Part 1
With this article, we provide an overview of the EBS financial reporting requirements with a focus on the considerations specific to scenario-based approach (SBA) models. -
Emerging Topics Community: Climate Change: An introduction with Actuarial Considerations
In this episode, Joe Alaimo interviews Joan Barrett to discuss climate change, highlighting their impacts on the environment and everyday life and the roles actuaries play in assessing risks and solutions related to climate change. -
Maximizing Communication in Excel 365 with Dynamic Arrays, Lambda Functions, and ChatGPT
This article explains the benefits of using Let, Lambda, and Lambda Helper Functions in your Excel workbooks. The benefits discussed are extracting logic from cell references, version-controlling business logic, and using AI to convert logic to other languages for faster development of products. -
Model Validation 101: Bring Your Calculus Mindset!
Ideas for using calculus concepts to validate actuarial models. Focus is on reserve valuation models under PBR. Includes examples based on a simple Excel model. -
Illusion of Rationality
This article explores the fundamental differences between human thinking and the way Large Language Models (LLMs) generate responses. It illustrates how human decision-making involves multiple, often evolving, acceptance criteria, whereas LLMs rely on statistical probabilities to determine the most likely next word. -
Emerging Topics Community: LLMs – Separating the Models From the Applications
Anders Larsen and Shea Parkes are back to explore more about LLMs. -
Emerging Topics Community: Understanding LLMs - Tokenization
Anders Larsen and Shea Parkes are back to explore the technical aspects of LLMs.
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Dive into insightful content. Gain practical knowledge. Explore the latest research and key information for future use. Discover the Emerging Topics Community, an online forum that focuses on three main topic areas–Modeling, Predictive Analytics and Futurism, and Technology.