LLM / NLP in Risk & Compliance
8 companies using llm / nlp technology in the risk & compliance sector. Large language models for NLP, chat, document understanding, and text generation
Directory
Companies by Funding
OpenAI
ChatGPT powers many fintech applications
Anthropic
Claude used in finance applications
Databricks
Unified data and AI platform
Palantir
AI data platform for finance
Kensho (S&P Global)
AI analytics for finance
DataSnipper
AI audit automation platform for auditors
Google DeepMind
AlphaFold methodology applied to finance
Leo RegTech
AI compliance platform with Eva assistant
Related
Other AI Technologies in Risk & Compliance
Related
LLM / NLP in Other Segments
Frequently Asked Questions
How is LLM / NLP used in risk & compliance?
Large language models for NLP, chat, document understanding, and text generation In the risk & compliance sector, LLM / NLP is applied by 8 companies tracked by AIFI Map, with $102.0B in combined funding. Leading companies include OpenAI (ChatGPT powers many fintech applications); Anthropic (Claude used in finance applications); Databricks (Unified data and AI platform).
Which risk & compliance companies use LLM / NLP?
The AIFI directory tracks 8 risk & compliance companies using LLM / NLP, with a combined $102.0B in funding. OpenAI, Anthropic, Databricks, Palantir, Kensho (S&P Global), and 3 more. (Source: AIFI Map directory.)
What is the most funded LLM / NLP risk & compliance company?
OpenAI is the most funded company using LLM / NLP in risk & compliance, with $57.9B raised. ChatGPT powers many fintech applications Followed by Anthropic ($27.3B).
How does LLM / NLP compare to other AI technologies in risk & compliance?
In the Risk & Compliance segment, LLM / NLP ranks #4 out of 8 AI technologies by adoption, with 8 companies and $102.0B in combined funding. The most widely adopted technologies in risk & compliance are Predictive ML (42 companies, $11.0B funded); Computer Vision (17 companies, $59.5B funded); Data Platform (11 companies, $17.9B funded). While LLM / NLP focuses on large language models for nlp, chat, document understanding, and text generation, alternative approaches include Predictive ML, which traditional ml algorithms for classification, regression, and scoring models, and Computer Vision, which image and video processing, ocr, and document extraction.