CNFINBENCH: A BENCHMARK FOR SAFETY AND COMPLIANCE OF LARGE LANGUAGE MODELS IN FINANCE

JinruDing, Chao Ding, Wenrao Pang, Boyi Xiao, Zhiqiang Liu, Pengcheng Chen, Jiayuan Chen, Dawei Cheng, Tiantian Yuan, Guan JunMing, JIE XU

Keywords: Safety and Compliance, Finance, LLM-judge ensemble, trustworthy AI

TL;DR: CNFinBench is a Chinese benchmark with a scalable judge ensemble that defines and evaluates financial LLM safety and compliance, revealing a gap between capability and compliance and enabling auditable alignment.

Abstract:

Large language models are increasingly deployed across finance—for research, compliance support, risk analysis, and customer service—making rigorous safety evaluation essential. However, prior financial benchmarks largely emphasize textbook-style QA and numeric problem solving while under-testing real-world safety: they weakly assess regulatory compliance and investor-protection norms,
seldom probe multi-turn adversarial tactics (e.g., jailbreaks, prompt injection, obfuscation), bind answers to long filings inconsistently, overlook tool/RAG risks, and rely on brittle or non-auditable judging. We introduce CNFinBench to close these gaps. CNFinBench organizes tasks under a Capability–Compliance–Safety triad, spanning evidence-grounded analysis of long financial reports, rule/tax rea-
soning, and finance-tailored red-team dialogues that conceal violations in realistic contexts. It enforces auditability via strict output formats for objective items(with dynamic option perturbation) and a scalable judge design (LLM-ensemble with human calibration) for free-form responses, and it evaluates tool-augmented workflows to surface RAG/agent injection and over-reach risks. Experiments on diverse models reveal a persistent capability–compliance gap: systems strong on structured tasks often falter on compliance auditing, risk disclosure, and evidence consistency; refusal alone is not a reliable proxy for safety without cited, verifiable reasoning. CNFinBench delivers reproducible metrics, attack templates, and scoring scripts to support admission control, regression testing, and alignment in
high-stakes financial settings.

Primary Area: datasets and benchmarks

Submission Number: 11074