AI USE CASE
AI Liquidity Forecasting for Treasury
Forecast daily cash positions across accounts and currencies to minimise idle cash and optimise liquidity buffers.
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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