LLM Accuracy on Financial Benchmarks Statistics

FinBen evaluated 15 leading LLMs across 36 datasets and 24 financial tasks spanning extraction, analysis, QA, generation, risk, forecasting, and decision-making

FinBen is among the most comprehensive open financial benchmarks and was the first to add stock-trading evaluation, giving a broad picture of where models succeed and fail.

On FinBen, GPT-4 led on quantification, extraction, numerical reasoning, and stock trading, while models broadly struggled with text generation and forecasting

The benchmark found strength concentrated in structured extraction and analysis and weakness in open-ended generation and prediction, a pattern that holds across the models tested.

A fine-tuned FinBERT reached about 0.88 accuracy and 0.87 F1 on the Financial PhraseBank sentiment task, competitive with or ahead of few-shot GPT-4o

The study found GPT-4o with few-shot examples could match a well fine-tuned FinBERT, indicating a small specialized model still holds its own on a focused finance classification task.

On the FinQA numerical-reasoning benchmark, the best published systems trailed expert human accuracy by a wide margin

FinQA pairs questions with earnings-report tables and text. The model-versus-expert gap shows multi-step financial arithmetic remains a structural weak point, even as newer models improve.

Domain-adapted financial models such as BloombergGPT outperformed comparable general models on finance tasks without sacrificing performance on general-language benchmarks, while still trailing far larger general models

Domain pre-training improved finance-task accuracy, confirming that specialization helps but does not by itself close the gap on harder reasoning.