INS-ActBench: A Comprehensive Benchmark for Assessing Professional Actuarial Capability of Large Language Models

2026-07-27 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Insurance & Risk Management · Depth: Advanced, quick

Summary

INS-ActBench is a new, comprehensive benchmark designed to evaluate the professional actuarial capabilities of Large Language Models (LLMs). It addresses limitations of existing benchmarks by assessing realistic workflows requiring auditable, context-grounded, and tool-executable decisions, rather than isolated skills. The benchmark comprises 12,050 Q&A pairs sourced from public exams and sample questions released by 16 actuarial associations. It is structured into three subsets: INS-Act-Know for standardized actuarial knowledge, INS-Act-Case for long-context insurance case reasoning, and INS-Act-Practice for spreadsheet and R-code tasks with verifiable numerical outputs. Initial experiments involving nine representative LLMs and human actuarial experts indicate that while frontier LLMs excel in standardized knowledge, they significantly underperform in case reasoning, tool-based workflows, and jurisdiction-sensitive practice. This benchmark offers a reproducible foundation for advancing actuarial LLMs towards reliable professional assistance.

Key takeaway

For AI Scientists and Machine Learning Engineers developing LLMs for actuarial applications, you should recognize that while current frontier models excel in standardized knowledge, they significantly underperform in complex case reasoning, tool-based workflows, and jurisdiction-sensitive practice. Prioritize your development efforts on enhancing LLM capabilities in these specific areas, particularly integrating robust R-code and spreadsheet task execution, to move towards truly reliable professional assistance.

Key insights

INS-ActBench reveals LLMs excel in actuarial knowledge but struggle with complex case reasoning, tool-based workflows, and jurisdiction-specific practice.

Principles

• LLMs demonstrate strong financial reasoning potential.
• Professional workflows demand auditable, context-grounded decisions.
• Integrated benchmarks are crucial for realistic capability assessment.

Method

INS-ActBench constructs a comprehensive evaluation by compiling 12,050 Q&A from 16 actuarial associations, categorizing them into knowledge, case reasoning, and tool-based practice, then testing LLMs against human experts.

In practice

• Focus LLM development on case reasoning.
• Integrate R-code and spreadsheet tasks.
• Address jurisdiction-sensitive practice gaps.

Topics

• Actuarial LLMs
• LLM Benchmarking
• Financial Reasoning
• Insurance Case Reasoning
• R-code Integration
• Spreadsheet Automation

Code references

• FDU-INS/INS-ActBench

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Domain Expert

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.