FFinRED: An Expert-Guided Benchmark Generation and Evaluation Framework for Financial LLM Red-Teaming
arXiv:2606.19887v1 Announce Type: cross Abstract: Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks. Financial LLMs face regulatory compliance violations, fraud facilitation, and systemic trust erosion that require targeted evaluation. We introduce FinRED, an expert-guided red-teaming framework for financial LLM safety evaluation developed with financial experts. FinRED uses a novel two-level taxonomy mapping global standards (e.g., FATF and EU DORA) to threats ranging from regulatory evasion to complex fraud, integrated with a scalable pipeline th
The rapid advancement and deployment of LLMs into critical sectors like finance necessitate robust safety and compliance frameworks to prevent immediate risks and adhere to evolving regulations.
This framework addresses a critical gap in LLM safety, moving beyond general adversarial scenarios to tackle specific, high-stakes financial risks like fraud and regulatory non-compliance, which could lead to significant systemic impact.
The development of expert-guided, finance-specific red-teaming for LLMs introduces a more targeted and effective evaluation standard, shifting how financial AI systems will be developed, audited, and deployed.
- · Financial institutions
- · AI safety researchers
- · Regulatory bodies
- · Compliance software providers
- · Unregulated LLM developers
- · Cybercriminals
- · Financial fraud perpetrators
Financial LLMs will be designed with integrated compliance and fraud prevention measures from inception.
Increased trust in financial AI applications leads to broader adoption and deeper integration into critical banking and trading systems.
New regulatory standards emerge globally, specifically mandating expert-guided red-teaming for all AI deployments in sensitive financial sectors.
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Read at arXiv cs.AI