Financial Governance and the CFO’s Perspective: Data, Control, and Compliance

A financial forecast can be updated to reflect the most recent week while simultaneously incorporating outdated collection deadlines, provisional production costs, and investments that have yet to be approved. The result is up-to-date from a formal standpoint, but its reliability depends on information with different statuses and update times.

For the CFO, this distinction affects the ability to decide how much to invest, what commitments to make, and when to take out a loan. It goes without saying that, even when monitoring cash flows, inaccurate data leads to suboptimal decisions.

What Information Does the CFO Need to Guide Decision-Making?

To guide decision-making, the CFO needs a comprehensive view of the company’s performance and financial position that links results, forecasts, risks, and capital requirements. This view encompasses profitability, liquidity, working capital, investments, and debt, with the ability to delve deeper into the causes of variances.

Consider, for example, a business unit that increases its revenue while at the same time experiencing a decline in cash flow. Sales figures may be positive, but credit terms granted to customers and increased inventory are tying up resources. To assess the situation, it is necessary to consider margins, accounts receivable, payment terms, and inventory together. Otherwise, there is a risk of misinterpreting revenue growth as an increased ability to finance growth.

The quality of this analysis also depends on the distinction between actual results and forecasts. An accounting balance, a won order, and a business negotiation have different levels of certainty. Combining them in the same analysis requires defined rules: corporate scope, period, currency, plan version, and assumptions used. The CFO must be able to distinguish between what has already happened and what the model assumes will happen.

Data Quality and Management Control: Where Risk Arises

System fragmentation becomes a financial problem when it leads to incompatible interpretations of the same phenomenon. A customer may have different codes in the CRM and ERP systems; one company within the group may classify a cost differently from the others; and the treasury department may be working with updated due dates that differ from those in the reports.

Under these conditions, manual reconciliations are time-consuming and involve steps that are difficult to trace. This risk also extends to management control: an apparently insufficient margin may be due to an incorrect allocation, while positive performance may mask costs that were recorded late.

Reliable financial data must be accurate, complete, timely, and consistent with the decision it is intended to support. In fact, the accuracy of an amount loses its value if the cost center needed to allocate it is missing or if the entry is made after the forecast has been finalized.

Governance therefore assigns specific responsibility for information and determines who corrects discrepancies. For example, the Finance department defines the rules for cost allocation, while Purchasing and operational functions oversee the information generated by their respective processes. Control also takes place at the source: incomplete master data should be caught before it spreads into reports and forecasting models.

Finance Digitization: What the 2026 Benchmarks Indicate

The KPMG Global AI in Finance 2026¹ report is based on a survey of 1,013 finance executives from 20 countries and 13 industries, conducted in March 2026, at organizations with annual revenues of at least $250 million. The findings therefore describe a sample of well-established companies operating in the global market, which can help us better understand the direction in which the digitization of financial management is heading.

These data measure corporate experiences and perceptions: the gap between met and exceeded expectations suggests that each project should be evaluated based on measurable results, rather than using the widespread adoption of AI as proof of its effectiveness.

A Gartner survey of 204 finance executives², conducted in March 2026 and published on July 20, also found that 45% focus their AI investments primarily on productivity, while 20% prioritize the quality of decisions. For financial governance, the data raises an interesting point: distinguishing between time savings and improvements in decisions regarding capital, risk, and growth.

ERP, Data Integration, and Analytics: Different Roles, Common Rules

The ERP system records transactions and links operational events to their accounting effects. A purchase, a delivery, or an investment thus feeds into the information used by the Finance department. The robustness of this foundation depends on the configuration of processes, master data, and controls over entries.
Data integration expands the scope to include information from treasury, CRM, production systems, and applications used by group companies. Integration also means ensuring that codes, hierarchies, and periods are comparable, while verifying that the transfer does not lose any documents or duplicate amounts. Each data flow should display the upload result, any anomalies, and the last update time.

Analytics and planning use this foundation to analyze variances and develop scenarios; its operational value then depends on the design of the business model and the quality of the inputs.

This architecture may include different platforms and data sources. Consistency stems from shared definitions and traceability: the margin discussed by the CFO must be traceable back to the revenues, costs, and rules that generated it.

Forecasting and Planning: Linking Profit and Cash Flow

Financial planning becomes verifiable when it explicitly identifies the drivers—that is, the variables that determine the results. Volumes, prices, production requirements, personnel costs, investments, and payment terms must have consistent effects on the income statement, balance sheet, and cash flow statement.
An increase in sales, for example, may require more purchases and higher inventory levels before it generates cash receipts. The plan must make this timeline visible, because financing growth also depends on the time lag between cash outflows and inflows.

Let’s clarify this with an example: a company with 36.5 million euros in annual credit sales, distributed evenly, collects an average of 100,000 euros per day. A ten-day increase in the average collection period implies approximately an additional one million euros tied up in accounts receivable, all other conditions being equal. This simplified calculation excludes VAT and seasonal variations; however, it illustrates why a change in commercial terms must be factored into the financial assessment.

A rolling forecast periodically updates the forecast while maintaining a defined time horizon. Its frequency must be consistent with the update schedules of the data sources. Each revision should retain the assumptions, the previous version, and the rationale for the changes; this makes it possible to determine whether the variance stems from the market, the available data, or a management decision.

AI in Finance: Applications and Limitations of AI in Financial Management

AI in finance encompasses a variety of applications, each requiring specific control criteria. Predictive models can estimate revenue, demand, or costs; anomaly detection systems identify unusual activity; and generative AI assists in retrieving information and preparing comments on the results.

For forecasting, validation focuses on the model’s ability to perform on data not used during training. A revenue forecasting model should be compared to a simple benchmark, such as contract due dates adjusted based on historical delays. It is also necessary to separate the training data from the evaluation data temporally, avoiding the use of information that was not available at the time of the forecast.

Error indicators should be chosen based on the observed variable. When it comes to net cash flows—which can approach zero or change sign—certain percentage measures become less informative. The absolute error in euros and checking for a systematic tendency to overestimate cash receipts are also useful. However, an error that is small on average can still conceal a significant risk precisely in the month when demand is highest.

Generative AI introduces a different kind of control. An explanation of the discrepancy is useful when it draws on verifiable data; the flow of the text alone does not prove the accuracy of the stated cause. Calculations and aggregations must be performed using verifiable tools, while the assistant can make the analysis more accessible. Correlations may suggest the need for further investigation, but establishing causality requires a review of the business process.

Compliance and Audit Trails: Ensuring That Numbers and Decisions Can Be Traced

Financial compliance requires that information and transactions be consistent with the obligations applicable to the company and with internal procedures. The scope depends on the corporate structure, markets, industry, and activities carried out; the platform must be configured to reflect this reality. Segregation of duties separates incompatible activities. For example, the person who updates a supplier’s bank account information should be distinct from the person who authorizes the payment, or subject to formalized countervailing controls. Digitization makes it possible to enforce these rules through roles, permissions, and workflows.

The audit trail tracks who performed a transaction, when it was performed, and what information was involved. In consolidation, it also covers adjustments, currency conversions, and intercompany eliminations. In planning, it includes versions, assumptions, and approvals. Data lineage adds the origin of the data and the transformations it underwent up to the final metric.

When AI is involved, the documentation must include the model used, the inputs, the outputs, and human validation. One methodological reference is the NIST AI Risk Management Framework 1.0³, which organizes risk management into the functions of Govern, Map, Measure, and Manage. However, we should note that this is a voluntary framework that provides a set of best practices without replacing compliance with regulatory requirements.

Assistants must also comply with data permissions. A user authorized to view only a specific business unit should receive responses based on that scope, including any natural-language queries. Actions that modify records or authorize payments require controls commensurate with their impact and clearly assigned responsibilities.

How CFO View Integrates Planning, Consolidation, and Control

Our CFO View solution combines two components: Planning, for financial and economic planning, and Consolidation, for preparing consolidated financial statements.

• Planning integrates data from sales, production, purchasing, and investments into the income, balance sheet, and cash flow budgets. What-if simulations allow you to assess the effects of changes in prices or volumes.
• Consolidation supports transactions such as intercompany eliminations and multi-currency conversions, with operational and statutory consolidation and transaction tracking.

These components come together in the Financial Boardroom. The goal is to provide management with a coordinated overview of results and alternatives, linking the business plan to its financial implications.

The relationship with governance becomes apparent, for example, when management evaluates a change in volumes: the planning process must link the new scenario to the assumptions of the departments involved and to its economic and financial effects. In consolidation, on the other hand, it is necessary to reconstruct the transactions that lead from the data of the individual companies to the group’s results. These are two distinct requirements, which call for explicit corporate rules and a consistent configuration of the solution.

Reggy’s Contribution to Financial Analysis

As part of our AI solutions, Reggy Cash Assistant serves as an assistant for financial data analysis and action recommendations for the CFO, integrated with SAP Analytics Cloud. Its use in financial analysis requires the design of connections, accessible data sources, and controls consistent with business processes.
Validating results and distinguishing between proposals and authorized actions remain part of the process. Governance remains an organizational responsibility even when access to information becomes conversational.

How to Measure the Effectiveness of Financial Governance

A project should be evaluated against a baseline defined prior to implementation. For closing and reporting purposes, it is useful to consider the time required to produce validated results, subsequent adjustments, and the hours spent on reconciliations. Speeding up the closing process while maintaining a high number of corrections shifts the workload to subsequent phases.

When planning, it is essential to assess the accuracy and timeliness of the forecast, by time horizon and metric: liquidity, revenue, and margins. For controls, open exceptions, resolution time, and the ability to obtain supporting evidence are also important. In the case of AI, the economic assessment must include integration, maintenance, and human review, in addition to the costs of the technology.

For the CFO, the value of financial governance becomes apparent when it is possible to move from identifying a variance to investigating its causes and evaluating alternatives. An investment may appear profitable on the income statement but may require reevaluation when compared to available cash or financing terms. In this process, CFO View supports planning and consolidation; verified data and traceable assumptions provide management with an effective basis for making and justifying decisions.

Do you want to optimize your company’s financial governance?
Talk to our experts and find out which digital solutions are right for your needs.

¹ Source: kpmg.com
² Source: gartner.com
³ Source: airc.nist.gov