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

2026-07-27

AI Summary

This study addresses the limitations of existing large language model (LLM) evaluation benchmarks, which inadequately assess the integrated capabilities required in actuarial practice—namely domain-specific knowledge, long-context reasoning, numerical computation, and tool usage. To bridge this gap, the authors introduce the first comprehensive actuarial benchmark, aggregating 12,050 authentic examination questions from 16 actuarial associations. The benchmark encompasses standardized knowledge, insurance case-based reasoning, and executable tool tasks (e.g., spreadsheet manipulation and R code generation), emphasizing auditability, context dependency, and jurisdictional sensitivity. Through a multidimensional dataset, an automated evaluation framework, expert-controlled experiments, and reproducible validation protocols, the study systematically evaluates nine leading LLMs against human actuaries. Results reveal that while state-of-the-art models perform competently on foundational knowledge, they exhibit significant deficiencies in case reasoning and practical implementation, highlighting critical limitations in deploying LLMs for specialized professional domains.

Abstract

Large Language Models (LLMs) have shown strong potential in financial reasoning, but existing benchmarks often evaluate domain knowledge, numerical reasoning, long-context understanding, and tool use in separate settings. This limits their ability to assess realistic professional workflows that require auditable, context-grounded, and tool-executable decisions. We introduce \textbf{INS-ActBench}, a comprehensive benchmark for evaluating professional actuarial capability in LLMs. INS-ActBench contains 12,050 Q\&A pairs from public exams and sample questions released by 16 actuarial associations. It covers three subsets: \textbf{INS-Act-Know} for standardized actuarial knowledge, \textbf{INS-Act-Case} for long-context insurance case reasoning, and \textbf{INS-Act-Practice} for spreadsheet and R-code tasks with verifiable numerical outputs. Experiments on nine representative LLMs and human actuarial experts reveal a clear capability boundary: frontier LLMs perform strongly on standardized knowledge, but remain much weaker in case reasoning, tool-based workflows, and jurisdiction-sensitive practice. INS-ActBench provides a reproducible foundation for developing actuarial LLMs toward reliable professional assistance. The code is available at https://github.com/FDU-INS/INS-ActBench.

Problem

• actuarial capability
• large language models
• professional benchmark
• financial reasoning
• comprehensive evaluation

Innovation

• actuarial benchmark
• large language models
• tool-augmented reasoning
• long-context case analysis
• verifiable numerical output