Actuarial field may benefit from advances in Master of Laws (LLMs) studies
The SOA Research Institute has published a comprehensive guide titled "Operationalizing LLMs: A Guide for Actuaries," providing insights into deploying Large Language Models (LLMs) for actuarial purposes within the insurance industry.
The guide, released in collaboration with OpenAI and Google, offers detailed guidelines for the responsible and ethical use of generative AI. Deploying an LLM can be achieved through APIs from major developers like ChatGPT, or independently, although the latter requires more control, complexity, and resources. Using an API is simpler, faster, and cost-effective compared to hosting an LLM independently.
The SOA's AI Research landing page serves as a repository of resources, including the monthly Actuarial Intelligence Bulletin, which keeps readers informed about advancements in actuarial technology and new AI research reports.
When choosing an LLM for responsible actuarial use, risk and ethics considerations are paramount. Key provider considerations include privacy and protection, risk and compliance, technology and reliability, bias, fairness and discrimination, transparency and explainability, accountability and responsibility, and adherence to AI ethics guidelines like UNESCO’s Recommendation on the Ethics of Artificial Intelligence and the National Association of Insurance Commissioners Principles on Artificial Intelligence.
It is essential to ensure that the chosen provider meets security and privacy standards. A panel of experts recently discussed the use of generative AI, specifically LLMs, in the insurance industry, emphasizing the importance of actuaries, experts in risk management and governance, in ensuring their responsible and ethical use.
The panel concluded that while current AI tools can boost productivity for some tasks, they have not yet evolved enough to replicate actuarial analysis and decision-making. Implementing LLMs in the insurance industry comes with challenges such as data privacy and security, regulation compliance, and ethical standards.
Despite these challenges, the panel identified several applications of LLMs, including coding assistance, digital assistant, data summarization and categorization, testing and model validation assistance, translation, research source attribution, and claims integration.
LLM benchmarks, assessment tools that compare the strengths and limitations of different LLMs, offer a means to evaluate and choose the most suitable model for specific tasks. The cloud offers a simpler solution for LLM deployment compared to building an independent server. There are various deployment methods for LLMs, ranging from software for beginners to more robust solutions for production environments.
The choice of an LLM depends on factors such as model size and computational requirements, task-specific performance, context window size, and cost vs. performance. The guide provides a valuable resource for actuaries and the insurance industry, offering a roadmap for the responsible and effective deployment of LLMs.
Read also:
- Exploring Harry Potter's Lineage: Decoding the Enigma of His Half-Blood Ancestry
- Elon Musk Acquires 26,400 Megawatt Gas Turbines for Powering His AI Project, Overlooks Necessary Permits for Operation!
- U Power's strategic collaborator UNEX EV has inked a Letter of Intent with Didi Mobility to deploy UOTTA(TM) battery-swapping electric vehicles in Mexico.
- Global Gaming Company, LINEUP Games, Moves Into Extensive Global Web3 Multi-Platform Gaming Network
%20studies){kind=link}