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Ant International Unveils 100 Products to Drive Agentic Financial Operations

Ant International has announced an artificial intelligence-native financial stack that includes nearly 100products across payment, account, foreign exchange, treasury and growth operations, the company said in a Friday (Sept. 18) press release. The product suite is built on Ant International’s two proprietary foundation models, including a payment foundation model called Antom 3-in-1 Transformer…

Ant International Unveils 100 Products to Drive Agentic Financial Operations

Ant International has unveiled an artificial intelligence-native financial stack consisting of nearly 100 products across payment, account, foreign exchange, treasury and growth operations. This suite of products relies on two proprietary foundation models: Antom 3-in-1 Transformer for payments and FalconTST, a time-series transformer for foreign exchange and liquidity forecasting.

The product lineup also includes enhanced security with a two-layered architecture and interoperability tools such as Agentic Mobile Protocol (AMP) and the blockchain-based WhaleRTP platform. Antom 3-in-1 Transformer has reportedly reduced chargeback rates by up to 87%, while FalconTST delivers a forecasting accuracy of over 93%, potentially cutting foreign exchange hedging and allocation costs by 30% to 60%.

The products will be rolled out this fall and winter to global markets, with Ant International CEO Peng Yang highlighting the company's focus on trust infrastructure, foundational FinAI models, and a full stack of AI-native solutions. Several major banks, including Barclays, Citi, Deutsche Bank, and Standard Chartered, are already utilizing FalconTST for improved foreign exchange risk management.

Ant International is also collaborating with Visa and Mastercard to create a know your agent interoperability framework, aimed at simplifying AI agent onboarding and identification across various networks.

Written by urgent.news from PYMNTS's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

Read the original at pymnts.com →

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