Global banks sign for Ant International AI model FalconTST to improve the cashflow forecasting and FX liquidity management capabilities
Banks use FalconTST 2.0 to better forecast liquidity exposure with Barclays, Citi, Deutsche Bank and Standard Chartered having integrated FalconTST 2.0 for cashflow forecasting and FX management. FalconTST 2.0 has achieved >93% forecast accuracy rate consistently and is ready for broader industry application, including logistics, aviation, e-commerce, besides financial sector.
Ant International has introduced Falcon Time-Series Transformer (TST) AI Model 2.0, “its most advanced TST model so far designed to deliver more accurate forecasting in real-world FX risk management of cross-border payments, with more industry applications to come, such as demand forecasting for supply chain management for e-commerce platforms, and predictive operations management for aviation industry” – according to the press release.
FalconTST 2.0 demonstrates State-of-the-Art (SOTA) performance on the Mean Absolute Scaled Error (MASE) metric on a top global public evaluation benchmark for time-series foundational models. MASE is among the most critical metrics used to evaluate time-series models. FalconTST 2.0 achieved a MASE score of 0.666 and places it at the top of the leaderboard, surpassing other TST foundational models from leading global tech companies.
FalconTST model is AI built for finance
While large language models excel at learning relationships in text, TST models are especially critical in finance and payments, where liquidity needs, foreign-exchange movements, and transaction flows can shift rapidly. The financial information consists of continuously changing numerical data — transaction amounts, account balances, settlement flows, and currency positions. For a global payment institution, these forecasts directly impact capital efficiency. The value of AI prediction lies not just in ‘calculating more accurately’ but in helping businesses know precisely when they need funds, how much they need, and in which currencies.
This forecasting capability is equally critical for foreign exchange management. An airline may collect ticket revenues in multiple currencies while needing to pay for aircraft leases, airport fees, and operating costs in different currencies. Companies typically use foreign exchange hedging to reduce currency fluctuation risk, but that requires them to determine how much of each currency they will receive and need. If forecasts are too high, they may over-hedge; if too low, they leave themselves exposed to foreign exchange risk.
Traditional forecasting systems typically build separate models for different tasks — a retail company trains a sales forecast model, an airline a demand forecast model, a financial institution a liquidity model. TST foundational models take a different approach: FalconTST learns common patterns — cycles, trends, seasonality, and sudden shifts—from data across finance, retail, energy, travel, and economics. Though these industries differ, the underlying temporal structures often share commonalities.
World’s leading banks using FalconTST to epitomise liquidity and FX management
The FalconTST is initially deployed internally at Ant International to manage cashflow and FX exposure on an hourly, daily and weekly basis, before having been integrated by leading global banks to their own FX hedging models, including Barclays, Citi, Deutsche Bank and Standard Chartered, for Ant International and its clients.
Barclays integrates the FalconTST Model into its FX hedging platform, BARX NetFX, while Citi combines FalconTST with their own Fixed FX Rates solution. They are mainly used for FX risk management on e-commerce platforms or airlines. And Standard Chartered uses the model alongside its SCALE FX system as part of both sides’ participation in the PathFin.ai programme of the Monetary Authority of Singapore.
Currently, they all have adopted the 2.0 version of the FalconTST Model, leading to an improved forecasting accuracy of more than 93% consistently. This level of precision is critical for financial institutions managing vast volumes of cross-border payments and needing to mitigate currency fluctuation risks effectively.
From a single financial tool to a reusable forecasting capability
Ant International said another goal of FalconTST is to make the same forecasting capability reusable across customers and industries. Aviation is a typical use case: revenues and costs span multiple currencies, while cash flows can change rapidly, making accurate forecasting critical. FalconTST is already being used for FX and liquidity management in the sector and is expanding into e-commerce, logistics and other industries.
If a capability serves only one customer, it remains largely a customised solution. When it can be reused across customers and industries, it starts to become a foundational capability.
“Large language models have shown how AI can understand and generate information. FalconTST is about another capability that businesses increasingly need: understanding how the world changes over time, and anticipating what comes next. For us, the value of AI is not simply achieving a better forecasting score, but turning that predictive intelligence into real decisions—how much liquidity to prepare, how to manage FX exposure, and how to allocate capital more efficiently. FalconTST 2.0 is an important step toward making predictive AI a foundational capability for global businesses, across payments, accounts and broader financial services,” said Jiang-Ming Yang, Chief Innovation Officer, Ant International.
“FalconTST helps global businesses — including our own — manage complex cash flow and FX exposure, so they can manage cross-border transactions with greater confidence. With FalconTST 1.0, clients saw real operational value and cost savings from better forecasting. With FalconTST 2.0, enhanced accuracy and precision let us extend those benefits to our banking partners as well as a broader range of customers across fast-moving sectors like e-commerce, travel and fintech,” said Kelvin Li, General Manager of Platform Tech and Senior Vice President, Ant International.
FalconTST 2.0 introduces several technical innovations that address real-world data challenges to improve forecasting accuracy:
- Advanced handling missing data. No bank transactions over a weekend does not mean demand is zero. FalconTST 2.0 distinguishes missing data from actual zero values, reducing misleading patterns.
- Powerful generalisation across domains. Through ORBIT, FalconTST learns common time-series patterns across finance, retail, energy and tourism, enabling direct forecasting in new business scenarios.
- Support for multiple time frequencies. From second-level payment data to hourly treasury needs, daily airline demand and monthly economic indicators, FalconTST 2.0 handles different frequencies within a single architecture.
Ant International invites interested developers worldwide to contribute feedback and accelerate innovation in time series learning through an API trial of FalconTST 2.0 via GitHub . To learn more about the FalconTST Model and recent success stories: https://falcon-tst.ant-intl.com/
