FINESSE-Bench: A Hierarchical Benchmark Suite for Financial Domain Knowledge and Technical Analysis in Large Language Models
arXiv:2605.15482v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly being applied to financial analysis, reporting, investment decision support, risk management, compliance, and professional training. However, robust evaluation of their domain competence in finance remains incomplete. Widely used open benchmarks such as FinQA, ConvFinQA, and TAT-QA have played an important role in advancing financial question answering and numerical reasoning, but they focus primarily on question answering over financial reports and do not provide an explicit hierarchy of professi
The proliferation of LLMs in finance creates an urgent need for robust, specialized benchmarks to accurately assess their domain-specific capabilities beyond general-purpose tests.
A hierarchical benchmark like FINESSE-Bench provides critical infrastructure for evaluating and developing AI models capable of handling complex financial tasks, impacting investment decisions and risk management.
The financial industry gains a more sophisticated tool for vetting AI models, moving beyond general benchmarks to those tailored for specific financial domain knowledge and technical analysis.
- · Financial LLM developers
- · Financial institutions adopting AI
- · Quantitative analysts
- · AI ethics and risk management platforms
- · AI models lacking financial specialization
- · General-purpose evaluation frameworks
- · Traditional financial analysis software
Improved performance and reliability of LLMs in financial applications due to targeted evaluation and development.
Increased adoption of AI in complex financial roles, leading to automation of decision-making and advisory functions.
Potential for new financial products and services enabled by highly specialized and trusted AI, altering market structures.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL