#fintech
Financial technology research, platforms, and infrastructure for modern accounting systems
FinMCP-Bench evaluates six LLM models on 613 real-world financial tool-use tasks backed by 65 MCP servers — the best model scores 3.08% exact match on multi-turn tasks, revealing a 20× performance collapse from single-tool to multi-turn scenarios.
FinTrace benchmarks 13 LLMs on 800 expert-annotated financial task trajectories across 9 metrics, finding that frontier models achieve strong tool selection (F1 ~0.9) but score only 3.23/5 on information utilization — the step where agents reason over what tools return.
FinToolBench pairs 760 live financial API tools with 295 executable queries to benchmark LLM agents on real financial tasks — revealing that GPT-4o's conservative 22.7% invocation rate yields higher answer quality (CSS 0.670) than Qwen3-8B's aggressive 87.1% TIR, while intent mismatch exceeds 50% across every model tested.
Bloomberg trained a 50B-parameter LLM on 569B tokens of financial data and beat general models on sentiment and table-reasoning benchmarks — then GPT-4 matched it without any finance-specific pretraining. What the $10M experiment reveals about domain pretraining trade-offs, tokenization of numbers, and why tool-use is more reliable than model internals for accounting agents.
