Computer Vision in Banking & Payments
7 companies using computer vision technology in the banking & payments sector. Image and video processing, OCR, and document extraction
Directory
Companies by Funding
OpenAI
ChatGPT powers many fintech applications
Incode
AI identity verification with deepfake detection
Onfido (Entrust)
AI document and biometric verification
Veriff
AI identity verification
Jumio
AI-powered identity proofing
AU10TIX
AI-powered ID authentication and fraud prevention
Sumsub
AI all-in-one verification platform
Related
Other AI Technologies in Banking & Payments
Related
Computer Vision in Other Segments
Frequently Asked Questions
How is Computer Vision used in banking & payments?
Image and video processing, OCR, and document extraction In the banking & payments sector, Computer Vision is applied by 7 companies tracked by AIFI Map, with $59.1B in combined funding. Leading companies include OpenAI (ChatGPT powers many fintech applications); Incode (AI identity verification with deepfake detection); Onfido (Entrust) (AI document and biometric verification).
Which banking & payments companies use Computer Vision?
The AIFI directory tracks 7 banking & payments companies using Computer Vision, with a combined $59.1B in funding. OpenAI, Incode, Onfido (Entrust), Veriff, Jumio, and 2 more. (Source: AIFI Map directory.)
What is the most funded Computer Vision banking & payments company?
OpenAI is the most funded company using Computer Vision in banking & payments, with $57.9B raised. ChatGPT powers many fintech applications Followed by Incode ($407M).
How does Computer Vision compare to other AI technologies in banking & payments?
In the Banking & Payments segment, Computer Vision ranks #3 out of 6 AI technologies by adoption, with 7 companies and $59.1B in combined funding. The most widely adopted technologies in banking & payments are Predictive ML (19 companies, $17.2B funded); LLM / NLP (9 companies, $86.0B funded); Computer Vision (7 companies, $59.1B funded). While Computer Vision focuses on image and video processing, ocr, and document extraction, alternative approaches include Predictive ML, which traditional ml algorithms for classification, regression, and scoring models, and LLM / NLP, which large language models for nlp, chat, document understanding, and text generation.