AI how-to guides Finance
How to forecast cash flow with AI
Connect an AI layer to your bank APIs, ERP entries, and intercompany flows to generate real-time cash positions and surface FX exposure before month-end close. The system automates data aggregation and reconciliation, allowing treasury to run on-demand liquidity stress scenarios instead of stitching together spreadsheets. Analysts review the netted proposals, lock in assumptions, and make funding decisions based on live signals.
What AI does
- Aggregates real-time data from bank APIs, SWIFT feeds, and ERP entries across entities and currencies.
- Surfaces live FX exposure and hedging coverage before the accounting period closes.
- Automates intercompany matching across entity ledgers and bank flows to propose netting opportunities.
- Runs on-demand liquidity stress scenarios, like customer payment delays or credit line cuts, with full lineage.
What stays with people
- Reviewing, adjusting, and approving intercompany netting proposals.
- Defining the FX exposure rules, netting policies, and the assumptions used in liquidity stress scenarios.
- Making the final hedging and same-day funding decisions.
How to put it in place
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Map your current treasury workflow and banking connectivity
Identify how bank statements, ERP entries, and intercompany flows are currently pulled and consolidated. Determine the critical entities, currencies, and bank accounts that need real-time visibility to support funding decisions.
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Define the rules for exposure and netting
Set up the position hierarchy, FX exposure limits, and your intercompany netting policy. Document which scenarios leadership and the board will need on demand, such as credit line cuts or FX shocks.
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Integrate banking APIs and enterprise systems
Connect the AI layer directly to bank platforms and SWIFT feeds to eliminate manual CSV uploads. Give the system access to ERP ledgers to normalise data across different currencies and accounts automatically.
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Validate positions against historical data
Run the automated cash flow aggregation in parallel with your traditional month-end close cycle. Compare the AI-generated real-time positions with the historical reconciled reports to verify data accuracy and fix integration gaps.
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Run scenarios and expand coverage
Begin using the live treasury view to run liquidity stress tests on demand with locked assumptions. Once the core workflow is stable, gradually expand the system to cover more entities and additional custom scenarios.
What you need
- Direct connectivity to bank APIs or SWIFT feeds to pull same-day statements.
- Access to ERP entries and entity ledgers to match intercompany flows.
- Clear, documented treasury policies for FX exposure and netting.
- A business owner in the treasury team to define scenario assumptions and review automated outputs.
When it is not worth it
- Small companies with a single bank account and currency, where spreadsheets are easy to maintain.
- Organisations without digital access to their banking data, relying on paper statements.
- When internal netting policies and treasury rules are not yet defined.
Risks and how to control them
- Incomplete data feeds: Missing a bank API or ledger can skew the cash position; ensure all critical accounts are fully integrated before relying on the dashboard.
- Algorithmic black boxes: Treasury needs to defend every number; require the system to maintain a full audit trail linking every position back to its source.
- Unauthorised access to sensitive financial data: Apply strict access controls and logging so only approved personnel can view FX exposures or run stress scenarios.
- Over-reliance on automated netting: The system might propose flawed matches; mandate human review and approval for all intercompany netting actions.
How Thinkia does it
We build a live treasury layer by aggregating signals from bank APIs, ERP entries, FX rates, and intercompany flows into a single governed view. We map your current workflow, integrate the data sources, and validate the positions against historical close cycles so you can act on real-time signals instead of stitching reports together.
The system surfaces live cash positions, FX exposure, and netting opportunities with a full audit trail. Treasury teams can run liquidity stress scenarios on demand, attach commentary, and publish defensible, board-ready outputs with complete ownership over the controls.
The Thinkia solution behind this guide
- Treasury cash-flow intelligenceLive cash, FX, and liquidity in one governed view
- AI financial forecastingRolling forecasts your CFO can defend
- AI risk intelligenceKnow your risk exposure before it becomes a risk event.
Thinkia products involved
Frequently asked questions
Can AI fully automate cash flow forecasting?
It automates the data gathering, normalisation, and scenario generation, giving you real-time positions. However, human treasury analysts must still lock in assumptions, review proposals, and make the actual funding decisions.
Do we still need to download CSV files from the bank?
No. The system connects directly to bank platforms and SWIFT feeds via API, eliminating the daily manual download and reconciliation ritual.
How does it handle different currencies and entities?
The AI normalises feeds across all connected entities, currencies, and accounts, providing a unified live view of FX exposure and unhedged risk before the accounting period closes.
Can we test what happens if a major customer pays late?
Yes. The platform includes an engine for liquidity stress scenarios where you can model customer delays, credit cuts, or FX shocks on demand and review the outcomes.
How do auditors verify the numbers?
Every aggregated position, FX exposure, and scenario output retains a full data lineage. It traces back to the source system, timestamp, and human reviewer, ensuring it is ready for internal and external audit.