Treasury teams are being asked to respond to a wider risk surface with forecasting processes built for a narrower world. CFOs and treasurers are tracking the right risks, yet risk awareness alone does not create response capacity.

In July 2026, the Federal Reserve held its target range at 3.50% to 3.75% after a 9-3 vote, with three governors preferring a rate increase. The decision came in the second meeting after Kevin Warsh became Fed chair. At the same time, the Federal Reserve reported that tariff increases were pushing up prices for some consumer goods. For a finance team, those developments create practical questions about borrowing costs, hedge positions, supplier pricing, working capital, and liquidity buffers.

Kyriba’s CFO Risk Radar tracks a broad set of exposures, from inflation, tariffs, interest rates, and currency volatility to supply chain issues, regulatory risk, workforce issues, AI disruption, and M&A activity. The risk categories may change, but the operational requirement remains consistent: finance leaders need timely, trusted information about how the organization’s liquidity position could change, along with the actions available in response.

The question is whether a treasury team has the forecasting maturity to respond across the risk landscape.

The readiness gap behind the risk list

The 2026 CFO Risk Radar makes the gap between confidence and capability concrete. Across the survey of CFOs and senior financial decision-makers, 47% said they felt highly prepared to manage financial risk. At the same time, 79% reported some degree of financial impact from inadequate risk visibility during the previous year.

Those findings measure different things, so they do not establish causation. They do, however, describe a meaningful preparedness gap. Confidence reflects how an organization views its readiness. Financial impact shows what can happen when emerging signals do not reach decision-makers quickly enough.

Cash forecasting maturity provides a practical way to locate that gap. It describes how effectively a finance organization can collect, reconcile, analyze, and act on liquidity data across banks, entities, currencies, and time horizons.

The model is a readiness lens. A platform can support progress, but the presence of a platform does not, by itself, mean that an organization can respond quickly to a liquidity question.

A simple test is to ask four questions:

• Does the team reconcile cash manually across multiple bank portals?

• Can finance leaders trust the forecast without several days of spreadsheet reconciliation?

• Can the organization model a new scenario without rebuilding the analysis manually?

• Can treasury teams see liquidity changes across entities and currencies as they occur?

The answers point to one of four stages.

Stage 1: manual operations

At the first stage, cash forecasting is primarily a manual operating process. Treasury teams pull balances from multiple bank portals, request updates from business units, consolidate data in spreadsheets, and reconcile differences by hand.

Consider a hypothetical manufacturer with dollar-denominated debt, euro operating costs, and suppliers in Japan. If rates rise, the yen moves, and a major customer delays payment, the treasury team may spend several days assembling the information needed to understand the combined effect.

Forecasting often follows an ad hoc or annual planning cadence. The organization can produce a cash position when a decision requires one, but it does not maintain a continuously refreshed view of liquidity. Reliability depends heavily on the experience of the individuals preparing the forecast.

A CFO can usually identify the tell at this stage: cash positions are reconciled manually across multiple bank portals, accounts, and entities. The organization is structurally late to risk because the process introduces too much delay.

Stage 2: spreadsheet-dependent

At the second stage, the process becomes more consistent, while spreadsheets remain the primary operating environment. Some bank data feeds may be automated, and treasury teams may have established templates, ownership rules, and review cycles.

The hypothetical manufacturer can now run quarterly scenarios for a stronger dollar, higher borrowing costs, or slower customer collections. When market conditions change between review cycles, however, the team still needs to update assumptions, reconcile new information, and circulate a revised workbook.

The tell at Stage 2 is familiar: finance leaders have a forecast, and they do not quite believe it until several days of reconciliation are complete. Spreadsheet capability can create a misleading sense of maturity because a sophisticated workbook may still conceal delayed data, version-control issues, and individual dependencies.

Stage 3: automated

At Stage 3, treasury teams use system-integrated processes to aggregate data across banks and entities. Reporting becomes standardized, recurring, and faster to produce. Finance leaders can access dashboards that provide a more consistent view of cash, debt, working capital, and liquidity performance.

The manufacturer can now compare a rate-hold scenario with a 25-basis-point increase, assess the effect of currency movements, and review the impact of delayed collections within a weekly or monthly process. That represents a significant improvement over quarterly spreadsheet reviews.

A remaining constraint appears when the question changes. An analyst may still need to define assumptions, select data, configure the scenario, and validate outputs manually for every new exercise. Automation improves speed and consistency, yet the organization still operates with a lag between the appearance of a risk and the ability to evaluate its liquidity impact.

Stage 4: autonomous liquidity intelligence

At Stage 4, forecasting moves from a recurring reporting activity to an always-on intelligence capability.

The manufacturer’s cash, debt, bank balances, foreign exchange exposures, payment forecasts, and working capital assumptions are continuously connected. When a rate decision, currency move, tariff-related cost change, or customer payment delay occurs, the organization can evaluate the effect across entities and currencies without starting a new data pull or rebuilding a spreadsheet.

Kyriba’s Japan research illustrates the response challenge. Only 15.8% of 101 Japanese CFOs and senior financial decision-makers said their organizations could adjust financial strategy on the same day a risk was identified. The finding makes the operational issue clear: risk awareness has limited value when decision processes move more slowly than the risk itself.

Stage 4 is the practical expression of the Risk Radar capability pillar, which calls for automated scenario analysis and cash flow forecasting that replaces quarterly snapshots with always-on financial risk intelligence.

The Stage 4 tell is simple: when the CFO asks the question, the answer already exists. The team may still need to interpret the result and decide what action to take, but it is not starting with a new data pull, spreadsheet build, or reconciliation cycle.

Kyriba’s platform capabilities support that operating model by unifying cash and liquidity data across the enterprise, connecting banks, ERPs, applications, and portals, and enabling multi-scenario forecasting. Embedded AI can extend analysis and surface predictions, while governance, data quality, and treasury judgment remain essential to deciding what the answer means and what action follows.

Why maturity matters more than risk awareness

The same forecasting foundation that helps a treasury team assess tariff exposure can support analysis of interest rate changes, currency swings, supplier disruption, customer collections, and funding pressure. Maturity creates a reusable response capability rather than a collection of point solutions for individual threats.

If a treasury team cannot produce a trusted cash position within 24 hours of a market event, the specific risk that triggered the question is secondary. The organization lacks the data foundation and operating speed required to respond.

Moving up the curve

The July FOMC decision, the transition to Kevin Warsh, and tariff-driven price pressure are different market events, but they can converge in the same treasury forecast through borrowing costs, hedge positions, supplier pricing, and liquidity buffers. The 12 risks on the CFO Risk Radar will continue to evolve in the same way. A future rate decision, tariff change, or currency move may look different from today’s headline, but finance teams will still need to translate each signal into a timely view of cash, liquidity, and available action.

The Risk Radar is an inventory of what is currently in motion. Forecasting maturity is the capability that extends beyond the list.