arXiv:2606.06235v1 Announce Type: new Abstract: Mortality forecasting plays an important role in actuarial and policy decision-making, but its implementation remains technically complex and inaccessible to non-expert users. This project proposes a reliable large language model (LLM)-integrated interface that improves usability while maintaining statistical power. The LLM is designed as a constrained orchestration layer that translates natural-language inputs into structured configurations for a deterministic forecasting pipeline. A three-phase methodology is employed to ensure accuracy, usabil
The proliferation of LLMs creates opportunities to simplify complex interfaces, and this research addresses the critical need for reliable integration in sensitive fields like actuarial science.
Improving accessibility to sophisticated analytical tools like mortality forecasting through reliable LLM interfaces can democratize their use and lead to better decision-making across various sectors.
The barrier to entry for non-expert users employing complex statistical models may decrease significantly, making advanced forecasting more widely applicable.
- · Actuarial science
- · Insurance industry
- · Policy makers
- · AI interface developers
- · Traditional statistical software providers
- · Complex, expert-only forecasting consultancies
More widespread and easier use of sophisticated mortality forecasting models becomes possible.
Increased accuracy and efficiency in risk assessment and policy formulation, potentially leading to more competitive insurance products and better public health strategies.
The success of this approach could accelerate the development of reliable LLM-integrated interfaces for other complex analytical domains, broadening AI's application beyond current expert use.
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Read at arXiv cs.LG