A fusion of Large Language Models (LLMs) and Large Quantitative Models (LQMs). We build foundational models that speak the native language of finance in both developed and frontier markets.
Generic LLMs (like GPT-4) are trained primarily on Western internet data. They exhibit severe bias against frontier markets, lacking the context to understand the unique financial dynamics of regions like Sub-Saharan Africa, MENA, and Southeast Asia.
Financial terminology varies by jurisdiction. A "bond" in New York functions differently than a "Sukuk" in Riyadh. Standard models fail to capture these crucial distinctions, leading to inaccurate strategic advice.
Quantitative Language Models are not just fine-tuned wrappers. They are foundational models designed to be complementary to existing generative AI.
By merging the linguistic fluidity of LLMs with the mathematical rigor of LQMs, FinanceGPT provides a robust solution for professionals operating in complex, data-sparse economies.
Benchmarking Performance Across Key Financial Tasks
| Model | Type | Avg. Score |
|---|---|---|
|
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FinanceGPT QLM
|
LQM + LLM | 94.5% |
| GPT-4 Turbo | General LLM | 88.2% |
| Claude 3 Opus | General LLM | 86.7% |
| Llama 3 (70B) | Open LLM | 81.4% |
| BloombergGPT | Finance LLM | 89.1% |
Models are only as good as the data they ingest. While others scrape the open web, FinanceGPT trains on FinCorpus—our proprietary, curation-first dataset.
Curated for Alpha
Contains millions of high-fidelity financial documents, earnings calls, and regulatory filings from underserved markets that do not exist in Common Crawl.
Sanitized for Logic
We rigorously filter out noise, marketing fluff, and hallucinations before training, ensuring our QLMs learn valid economic relationships.
Empower your strategy with AI that understands your market.