Data Platform in Risk & Compliance
11 companies using data platform technology in the risk & compliance sector. Data aggregation and enrichment platforms with minimal ML
Directory
Companies by Funding
Databricks
Unified data and AI platform
Palantir
AI data platform for finance
Dataminr
AI for real-time event and risk detection
Clarity AI
AI sustainability analytics platform
Notabene
AI for crypto travel rule compliance
Ascent RegTech
AI regulatory intelligence platform
Compliance.ai (Archer)
ML-powered regulatory change management
FinregE
AI regulatory compliance automation
Related
Other AI Technologies in Risk & Compliance
Related
Data Platform in Other Segments
Frequently Asked Questions
How is Data Platform used in risk & compliance?
Data aggregation and enrichment platforms with minimal ML In the risk & compliance sector, Data Platform is applied by 11 companies tracked by AIFI Map, with $17.9B in combined funding. Leading companies include Databricks (Unified data and AI platform); Palantir (AI data platform for finance); Dataminr (AI for real-time event and risk detection).
Which risk & compliance companies use Data Platform?
The AIFI directory tracks 11 risk & compliance companies using Data Platform, with a combined $17.9B in funding. Databricks, Palantir, Dataminr, Clarity AI, Notabene, and 3 more. (Source: AIFI Map directory.)
What is the most funded Data Platform risk & compliance company?
Databricks is the most funded company using Data Platform in risk & compliance, with $14.0B raised. Unified data and AI platform Followed by Palantir ($2.5B).
How does Data Platform compare to other AI technologies in risk & compliance?
In the Risk & Compliance segment, Data Platform ranks #3 out of 8 AI technologies by adoption, with 11 companies and $17.9B 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 Data Platform focuses on data aggregation and enrichment platforms with minimal ml, 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.