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

arXiv: https://arxiv.org/abs/2607.24273 · HTML: https://mlsanity.com/paper/2607.24273/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 27 Jul 2026.

## Builds on (most cited first, 12 of 12)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance](https://mlsanity.com/paper/2105.07624/) (2021)
- [FinanceBench: A New Benchmark for Financial Question Answering](https://mlsanity.com/paper/2311.11944/) (2023)
- [MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data](https://mlsanity.com/paper/2206.01347/) (2022)
- [INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent](https://mlsanity.com/paper/2412.18174/) (2024)
- [DocFinQA: A Long-Context Financial Reasoning Dataset](https://mlsanity.com/paper/2401.06915/) (2024)
- [FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging](https://mlsanity.com/paper/2506.05828/) (2025)
- [StatLLM: A Dataset for Evaluating the Performance of Large Language Models in Statistical Analysis](https://mlsanity.com/paper/2502.17657/) (2025)
- [INS-MMBench: A Comprehensive Benchmark for Evaluating LVLMs' Performance in Insurance](https://mlsanity.com/paper/2406.09105/) (2024)
- [FinSheet-Bench: From Simple Lookups to Complex Reasoning, Where LLMs Break on Financial Spreadsheets](https://mlsanity.com/paper/2603.07316/) (2026)
- [INSEva: A Comprehensive Chinese Benchmark for Large Language Models in Insurance](https://mlsanity.com/paper/2509.04455/) (2025)
- [Design, Results and Industry Implications of the World's First Insurance Large Language Model Evaluation Benchmark](https://mlsanity.com/paper/2511.07794/) (2025)

## Built on by (most cited first, 1 of 1)

- [InsClaimBench: Benchmarking Insurance Claim Adjudication Across the Decision Chain](https://mlsanity.com/paper/2610.09671/) (2026)

## Abstract (arXiv)

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 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: 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. 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.