AI USE CASE
AI Liquidity Forecasting for Treasury
Forecast daily cash positions across accounts and currencies to minimise idle cash and optimise liquidity buffers.
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Run the diagnostic →What it is
Machine learning models analyse historical transaction flows, payment schedules, and market data to predict daily cash positions across multiple accounts and currencies. Treasurers gain a rolling 30-90 day liquidity outlook, enabling tighter buffer management and reducing idle cash by 15-30%. Automated alerts flag shortfalls or surpluses in advance, cutting manual reconciliation time by up to 50% and lowering short-term borrowing costs.
Data you need
At least 12-24 months of daily transaction data across all bank accounts and currencies, including inflows, outflows, and payment schedules.
Required systems
- erp
- accounting
- data warehouse
Why it works
- Centralise all account and transaction feeds into a single data layer before modelling begins.
- Involve treasury operators in model validation to build trust and ensure outputs align with business intuition.
- Establish a monthly retraining and backtesting cycle to maintain forecast accuracy over time.
- Start with a single currency or entity as a pilot before rolling out multi-currency forecasting.
How this goes wrong
- Fragmented banking relationships and siloed account data prevent a consolidated cash view, undermining forecast accuracy.
- Model drift when macroeconomic conditions or payment behaviours shift significantly, without a retraining cadence in place.
- Low adoption by treasury staff who distrust model outputs and continue relying on manual spreadsheets.
- Insufficient historical data depth or quality in accounts with irregular or seasonal cash flows.
When NOT to do this
Do not deploy AI liquidity forecasting at a company with fewer than three bank accounts and simple, predictable cash flows, a spreadsheet model will deliver equivalent accuracy at a fraction of the cost.
Vendors to consider
Sources
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