As 46% of CFOs prioritise enhanced forecasting, real-time transaction data and AI are transforming how finance teams manage liquidity, optimise working capital and respond to emerging financial risks. The competitive advantage now lies in turning continuous financial visibility into faster, more informed decisions.
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Finance leaders are moving beyond the traditional reporting cycle. Enhanced modelling and forecasting is shifting from a periodic back-office exercise into an operational capability, giving CFOs greater visibility over liquidity, working capital and commercial performance as financial conditions change.
The American Express CFO Survey 2026, which polled 999 senior finance executives, VPs and CFOs across 14 countries, reveals a distinct change in priorities. The proportion expecting enhanced modelling and forecasting to become an increasing part of their remit over the next 12 months has risen from 36% to 46%. At the same time, broader scenario modelling within the current CFO remit has fallen from 41% to 24%. The direction is towards more granular financial forecasting, focused less on modelling distant possibilities and more on understanding what is happening across the business now.
This transition is being enabled by increasingly continuous financial data. The survey highlights the use of real-time financial transaction data, with 50% already applying it to improve cash flow forecasting and working capital management and 47% using it to increase the accuracy and timeliness of board and investor reporting.
Rather than waiting for the monthly close to establish financial position, advanced finance functions are building forecasting models capable of responding continuously to transaction activity.
The mechanics of granular forecasting rely on connecting financial systems that have traditionally operated across different reporting cycles. Enterprise resource planning (ERP) records, accounts receivable, accounts payable, banking data and treasury positions become continuous inputs into a common forecasting environment.
| Data Mechanism | Finance Impact | |
|---|---|---|
| 1. Transaction Input | Continuous feeds from ERP, banking, AR and AP systems. | Establishes an up-to-date view of inflows, outflows and outstanding positions. |
| 2. Forecasting Layer | Models evaluate actual transaction behaviour against expected cash movements. | Continuously recalibrates liquidity and working capital projections. |
| 3. Variance Detection | Actual payment and collection patterns are assessed against forecast assumptions. | Identifies emerging cash shortfalls, delayed receivables or unexpected liquidity earlier. |
| 4. Operational Deployment | Updated forecasts feed treasury and finance decision-making. | Enables finance teams to adjust working capital decisions throughout the reporting period. |
| 5. Strategic Reporting | Current financial data feeds management, board and investor reporting. | Improves the timeliness and accuracy of financial narratives provided to stakeholders. |
The result is a shift from forecasting at fixed intervals towards maintaining a continuously evolving financial position.
This increased granularity arrives as CFO attention moves firmly towards finance fundamentals. Cash flow and finance management now account for 74% of the CFO remit, up from 65%, while financing is currently top of mind for 51% of executives. Working capital or top-line growth is a concern for a further 34%.
The response is increasingly centred on better forecasting. In the survey, Some of the 64% of executives intend to improve cash flow forecasting, up from 49% last year, while 60% are focused on optimising working capital, up from 51%. When faced specifically with a potential lack of working capital, the proportion turning to improved forecasting has risen from 32% to 44%.Continuous transaction visibility changes how these strategies can be executed.
A deterioration in customer settlement behaviour can immediately alter projected cash inflows. Changes in purchasing activity can update expected outflows. Unexpected receipts can reveal excess liquidity earlier than a month-end process. Instead of discovering these movements retrospectively, treasury can incorporate them into short-term cash positions while there is still time to respond.
External research points to the infrastructure challenge beneath this transition. PwC’s 2025 Global Treasury Survey found that 38% of organisations with revenues above $10 billion and 52% of organisations between $1 billion and $10 billion still manually collect and consolidate forecasting data. Data quality was identified as a challenge by 76% of respondents.
For CFOs, granular forecasting therefore depends as much on financial architecture as modelling sophistication. Automated data streaming reduces the manual hand-offs between ledger, treasury and reporting environments, allowing the forecast to respond to transaction activity rather than waiting for data to be assembled.
Artificial intelligence extends this model further.
Implementing AI has risen from 39% to 57% as a digital transformation responsibility among CFOs in the American Express research, while traditional cloud implementation has fallen from 30% to 7%. The survey is also beginning to identify tangible applications of AI across cash flow visibility, forecasting accuracy and working capital management.
In advanced forecasting environments, machine learning can evaluate historical customer settlement patterns, payment behaviour and changing transaction volumes against current ledger positions. Rather than simply highlighting that actual performance has deviated from budget, predictive models can identify where future cash positions are beginning to diverge from expectations.
The distinction changes the role of variance analysis.
Traditional reporting asks why performance differed from the forecast. Continuous forecasting creates the opportunity to identify emerging variance before the reporting period closes, allowing finance teams to investigate its cause and adjust operational assumptions earlier.
This creates a feedback loop: Live transactions -> updated financial position -> revised forecast -> variance identification -> operational response
The forecast therefore becomes less of a static financial output and more of an active control mechanism across the finance function.
Consider a multinational enterprise managing cash across several operating entities. Under a traditional model, treasury teams consolidate balances, AR positions and expected payments periodically before updating the organisation’s short-term liquidity forecast.
Connecting these systems creates a different operating model.
As invoices are settled, incoming transaction data updates expected collections. If a major customer begins paying later than its historical pattern, projected inflows are adjusted. At the same time, AP data provides visibility into upcoming supplier obligations, while bank feeds confirm the organisation’s current liquidity position.
Finance can then evaluate the emerging position against working capital requirements rather than waiting for the next forecast cycle.
Where liquidity tightens, discretionary expenditure or payment timing can be reassessed. Where projected cash exceeds expectations, finance can evaluate investment, debt reduction or supplier-payment opportunities earlier. Commercial decisions become connected to the latest financial position rather than a snapshot produced several weeks previously.
The competitive advantage is therefore not forecasting accuracy in isolation. It is the reduction in time between detecting a financial change and acting on it.
The same infrastructure is changing external and internal reporting.
Almost half (47%) of CFOs surveyed are already using real-time transaction data to improve the accuracy and timeliness of board and investor reporting.
Continuous financial visibility allows finance teams to identify material deviations throughout the reporting period, investigate their drivers and assess their implications before information reaches senior stakeholders.
This matters because greater reporting speed without greater confidence in the underlying data creates little strategic value. Integrating transactional systems with forecasting and reporting environments gives CFOs a more current evidence base from which to explain movements in cash, revenue and working capital.
It also changes the nature of board reporting. Instead of relying predominantly on retrospective variance explanations, finance can increasingly connect historical performance with current transaction patterns and forward-looking expectations.
Moving from periodic reporting towards continuous forecasting introduces new control requirements.
Real-time data must be accurate, consistently defined and traceable across systems. Predictive models require transparent assumptions. Material forecast changes need defined escalation thresholds, while human oversight remains necessary where automated analysis begins influencing significant financial decisions.
These controls are particularly important as AI becomes more embedded in forecasting workflows. Despite AI becoming the leading digital transformation responsibility in the American Express research, 20% of CFOs identify it as an area they feel least confident adapting to. Regulation, data quality and internal adoption also remain prominent implementation concerns.
Governance therefore becomes part of the forecasting architecture rather than a separate compliance exercise.
The evolution from monthly forecasting towards continuous financial intelligence does not eliminate uncertainty. It changes how quickly finance can recognise and respond to it.
Connecting real-time transaction data with granular forecasting gives CFOs a continuously updated view of liquidity, working capital and commercial performance. Finance functions that establish the data infrastructure, modelling capability and governance required to operate this way move forecasting beyond a reporting obligation, establishing it as an active engine for financial decision-making.
Sources:
American Express, The CFO Balancing Act: Grounded in Fundamentals, Powered by AI, American Express CFO Survey 2026 (https://www.americanexpress.com/en-gb/resources/white-papers/chief-financial-officer-survey-2026/);
PwC, 2025 Global Treasury Survey (https://www.pwc.com/us/en/services/consulting/finance-accounting-transformation/library/2025-global-treasury-survey.html);