Casap raises $56M Series B for bank dispute and anti-fraud AI

Published Monday, November 2, 2026 · 1:00 AM EST

Casap raised a $56M Series B led by Base10 Partners, bringing its total funding to $90M, Fortune reported. The company's AI tools automate dispute and anti-fraud operations for banks, placing the financing in software that handles those banking functions.

Sources

Techmeme

Published by Tech & Business, a media brand covering technology and business. This story was sourced from Techmeme and reviewed by the T&B editorial agent team.

Back to Newswire

Keep reading

Full wire

Capital Infrastructure

Braid raises €34.13 million for branded mobile connectivity platform

The Hague-based Braid raised €34.13 million ($38 million) in Series A funding led by Octopus Ventures, EU-Startups reported. The Dutch company's API lets enterprises sell eSIM mobile plans under their own brands through more than 600 carriers.

AI Science

TamilEOT releases open models for detecting when Tamil callers finish speaking

Researchers released TamilEOT, an open dataset and two models that detect whether a Tamil telephone caller has finished speaking or is pausing.

AI Infrastructure

ADNOC signs multiyear AI deployment agreement with OpenAI and G42

ADNOC announced a multiyear agreement with OpenAI and G42 on November 2 to develop and deploy AI agents that connect operational, capital and commercial decisions across its energy business.

Capital Infrastructure

Seamoretech secures €1.1 million for brine recovery technology

Porto-based Seamoretech secured €1.1 million in the first closing of its seed round, bringing its total funding to €3 million. Rypples, 7Seas Capital and Crédito Agrícola participated.

Security AI

Sophos reports Aur0ra attackers used AI agents to gain network privileges

Aur0ra ransomware operators used AI agents to navigate victim networks, collect credentials and obtain domain administrator access, Sophos found, Help Net Security reported.

AI Science

MOSAIC preprint reports better task performance with 72% fewer backbone parameters

Researchers report that MOSAIC, an architecture for learning from multiple types of input, improved average macro-F1, a classification performance measure, by 7.4 points across four tasks while using 72% fewer backbone parameters than a fusion-based baseline.