Design a Reliable LLM-Integrated Interface for Mortality Forecasting

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, extended

Summary

The project proposes a reliable large language model (LLM)-integrated interface for mortality forecasting. This system enhances usability for non-expert users while maintaining statistical power, accuracy, and transparency in high-stakes actuarial workflows. The LLM acts as a constrained orchestration layer, translating natural-language inputs into structured configurations for a deterministic forecasting pipeline. A three-phase methodology was employed: first, implementing a baseline pipeline using the CoMoMo package to reproduce established results; second, extending it for multi-step forecasts using rolling-origin evaluation and mean squared error (MSE); and third, developing a prototype interface with a local LLM to process plain-language requests. The system demonstrates that LLMs can improve accessibility without compromising reproducibility or actuarial validity. Ensemble methods, like stacked regression, consistently achieved lower MSE across nearly all forecast horizons compared to individual models.

Key takeaway

For actuarial and risk analysts seeking to democratize complex mortality forecasting, you should consider integrating LLMs as a constrained orchestration layer. This approach allows non-technical users to configure and run sophisticated models via natural language, significantly reducing manual effort and error. Prioritize deterministic statistical computation and transparent artifact generation to maintain auditability and statistical integrity, ensuring accessibility does not compromise reliability in high-stakes decision-making.

Key insights

LLMs can enhance complex analytical systems by acting as a natural-language orchestration layer, preserving statistical rigor.

Principles

• LLMs serve as orchestration layers.
• Deterministic pipelines ensure reproducibility.
• Ensemble methods enhance forecast accuracy.

Method

A three-phase approach: replicate baseline CoMoMo, extend to multi-step forecasting with rolling-origin MSE, then integrate a local LLM interface for natural-language configuration.

In practice

• Use CoMoMo for mortality forecasting.
• Implement rolling-origin evaluation.
• Integrate local LLM for user input.

Topics

• Mortality Forecasting
• LLM Integration
• Actuarial Analytics
• CoMoMo Package
• Ensemble Models
• Human Mortality Database

Best for: Machine Learning Engineer, AI Scientist, Research Scientist, AI Engineer, Data Scientist, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.