Ant International’s FalconTST Model 2.0 Achieves SOTA, Elevating Predictive AI Application in Finance to New Levels
FalconTST 2.0 has achieved state-of-the-art performance on MASE metric on top global benchmark.
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.
SHANGHAI & SINGAPORE, August 20, 2026--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.
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.