Design a Reliable LLM-Integrated Interface for Mortality Forecasting

Researchers propose an LLM-integrated interface for mortality forecasting that translates natural language inputs into structured actuarial predictions while maintaining statistical rigor. The system uses a constrained orchestration layer to enhance accessibility for non-expert users without compromising reproducibility or analytical validity in high-stakes forecasting workflows.

This research addresses a critical accessibility gap in actuarial science by demonstrating how large language models can democratize complex forecasting tasks without sacrificing accuracy. Mortality forecasting has traditionally required specialized expertise, limiting its adoption among policy makers and analysts lacking deep statistical training. The proposed three-phase methodology—establishing baseline accuracy, extending to multi-step forecasts, and implementing an LLM interface—provides a replicable framework for translating expert-driven processes into user-friendly systems.

The work emerges from broader trends in AI-assisted domain expertise, where language models serve as translation layers between technical systems and general users. By constraining the LLM to act as an orchestration engine rather than a forecasting engine itself, the researchers preserve the integrity of the underlying deterministic pipeline built on established packages like CoMoMo. This hybrid approach mitigates hallucination risks and ensures reproducibility—critical requirements in actuarial applications where inaccuracy directly impacts financial forecasts and policy decisions.

For the actuarial and insurance industries, this represents a significant usability advancement that could accelerate adoption of quantitative forecasting methods among organizations with smaller analytical teams. The transparency and explainability benefits extend beyond practitioners to regulators and stakeholders who require auditable decision-making processes. The framework's modular design suggests applicability to other high-stakes analytical domains requiring both accessibility and reliability.

Future development should focus on validation across diverse mortality datasets, integration with existing actuarial software ecosystems, and formal testing of the interface's ability to reduce forecasting errors among non-expert users.

• →LLM-integrated systems can enhance accessibility to complex forecasting without compromising statistical accuracy or reproducibility.
• →Constraining LLMs as orchestration layers rather than predictive engines mitigates hallucination risks in high-stakes applications.
• →The three-phase methodology successfully bridges baseline validation, extended forecasting, and user interface implementation.
• →Mortality forecasting accessibility improvements have direct applications for insurance, pension planning, and public health policy.
• →Hybrid human-AI systems that preserve deterministic pipelines offer a viable model for regulated industries requiring transparency.