AI in Finance and Accounting: Driving Efficiency Without Compromising Your Compliance

Today’s CFOs face a high-stakes balancing act.

• On one side, inefficient, manual processes are slowing down Finance and Accounting teams and limiting their strategic impact.
• On the other hand, mounting pressure to modernize is paired with real concerns over compliance, auditability, and financial data security.

What Finance leaders need are streamlined workflows and accelerated insight that still meet the highest standards for accuracy, transparency, and regulatory compliance.

Purpose-built AI for Finance can do exactly that.

In this post, you’ll learn what to look for in an AI solution that is built for control, not risk, so you can strike that balancing act for your team.

To deliver real value without increasing risk, AI cannot simply be bolted onto financial workflows. It must be designed specifically for them.

Most Finance and Accounting leaders understand that AI can drive efficiency and value by automating routine tasks, improving forecasting accuracy, and accelerating the delivery of deeper insights.

In fact, 30% of senior FP&A professionals say automation is their top priority over the next 12 months, according to Planful’s 2025 Global Finance Survey.

However, the same survey found that despite 60% of leaders reporting daily use of AI, many tools remain disconnected from the core systems and workflows that power Finance and Accounting.

This siloed, surface-level adoption limits AI’s impact and prevents organizations from applying the governance, auditability, and validation controls that financial processes demand.

Finance leaders need AI that understands general ledger structures, dimensional hierarchies, region‑specific accounting standards, and the regulatory scrutiny surrounding financial reporting, and that is embedded directly into planning and reporting workflows with controls built in from the start.

If AI offers so much potential to create efficiency at scale, what’s holding back Finance and Accounting teams from fully adopting it?

For many CFOs, the hesitation stems from real concerns around regulatory and data security. Finance operates under some of the most rigorous and rapidly changing regulatory frameworks of any sector. Falling out of step can lead to serious consequences, including steep penalties, missed forecasts, audit failures, financial restatements, and reputational damage.

AI adds another layer of complexity. The highly sensitive nature of financial data means that any system that touches it must meet the highest standards for security, accuracy, transparency, and control.

It’s no surprise, then, that our survey found that 44% of Finance leaders cite data privacy and security concerns as the top barrier to AI implementation.

And the problem isn’t just risk aversion from CFOs. Many AI tools simply aren’t built for regulated financial environments:

• They often operate as “black boxes,” producing results with no audit trail or explanation, undermining trust in the results.
• They rely on unproven third-party models to process financial data that can introduce compliance risks.
• They rely on generic third-party models that are not designed for Finance and lack the domain understanding required for accurate forecasting or reporting.
• They lack human validation, which is essential for regulatory reporting and internal assurance.

CFOs aren’t opposed to innovation. They simply won’t sacrifice financial data security, accuracy, control, transparency, or regulatory compliance in a high-stakes environment where getting it wrong comes with real consequences.

Having the right strategy is what separates AI that creates risk from AI that delivers results.

Here are three ways Finance and Accounting leaders like you can adopt AI while maintaining control, accuracy, and compliance.

Choose AI that is purpose-built for finance

Generic AI models aren’t designed for the nuance or complexity of financial data.

Forecasting accuracy, for example, depends on understanding the unique structure of general ledger data, dimensional roll-ups, and how regional accounting standards vary.

Purpose-built AI, embedded natively within financial performance management platforms, is trained on the workflows, hierarchies, and validations Finance teams rely on.

This ensures predictive models reflect the real complexities of Finance and deliver insights that are accurate and actionable.

Make explainability a requirement

Black box AI that delivers outputs without explaining how it arrived at those results is a non-starter in highly-regulated financial environments.

Every AI output must be supported by clear, traceable logic, demonstrating how conclusions were reached and which data points influenced them.

In addition to ensuring financial data security compliance, explainable AI also builds internal confidence. When teams understand how AI reaches its results, they’re more confident in sharing and acting on those insights to drive decisions.

Start with high-ROI, low-risk use cases

CFOs do not need to transform everything at once. Finance leaders should first focus on use cases where AI delivers clear value while keeping risk to a minimum:

• Anomaly detection to catch errors in data and resolve issues quickly
• Intelligent forecasting to generate scenarios based on department-specific data and drivers
• In-app assistance for instant answers, easier access to information, and clearer explanations of results
• Automated visualizations, like tables and charts, that help teams understand and communicate insights

Each of these use cases proves that AI can serve Finance and Accounting without introducing unnecessary risk.

These strategies are already in use by today’s forward-thinking Finance teams who are leveraging AI platforms designed specifically for their needs.

Unlike generic AI tools, Planful’s AI-driven solutions are purpose-built for Finance and Accounting.

Planful is designed around persona-based workflows and tailored to the unique real-world needs of Finance teams like yours, delivering strategic value without compromising regulatory compliance and financial data security.

Here’s how:

Compliance by design

Planful AI is built with controls in place from the start.

From role-based access to audit-ready output tracking, every Planful AI capability aligns with rigorous security and reporting standards.

The platform aligns with global regulatory frameworks and evolves alongside them, ensuring Finance teams always stay ahead of emerging regulations such as the EU AI Act.

Guardrails that empower Finance

Every AI-generated insight in Planful AI is explainable, supporting both internal reviews and external audits. Users can validate, adjust, or reject outputs before those outputs ever impact reporting or planning, which keeps Finance in control.

Embedded, not bolted on

Planful AI is integrated directly into the budgeting, forecasting, and reporting tools Finance teams already use. There’s no need to export data to external models or platforms, which means reduced risk, increased adoption, and accelerated time to insight.

CFOs no longer have to choose between AI-driven efficiency and regulatory compliance.

Planful’s Finance-specific AI has built-in safeguards that deliver faster insights, streamlined workflows, and more accurate forecasts, without sacrificing financial data security or transparency.

• Purpose-built AI matters: Generic tools that aren’t trained on the nuances of financial data or compliance standards can’t provide the level of accuracy, control, or data security and privacy that Finance requires.
• AI adoption in Finance demands transparency: Finance teams need automated solutions that provide clear, explainable outputs and maintain human oversight at every step.
• Efficiency and compliance aren’t mutually exclusive: With the right AI tools, CFOs can accelerate faster, more accurate forecasting and reporting without compromising financial data security or regulatory requirements.

When AI is purpose-built for Finance and designed with compliance in mind, it becomes a driver of value, performance, and control.

 

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Book a demo to learn more about Planful AI.

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How can Finance teams ensure AI stays compliant with evolving regulations?

Look for platforms that are updated in line with regulatory frameworks, such as SOC 1, SOC 2, SOC 3, ISO, HIPAA, and the EU AI Act. Look for built-in audit trails, data access controls, and guardrails that treat compliance as a core feature, not an afterthought.

How does AI improve financial forecasting?

AI accelerates and strengthens forecasting by analyzing large volumes of historical and real-time data to identify patterns, detect anomalies, and generate predictive scenarios. This enables Finance teams to build more accurate, dynamic forecasts faster and with greater confidence.

What makes AI adoption successful in Finance?

Successful AI adoption depends on alignment across Finance, IT, and compliance teams, as well as clear workflows and controls. The right tools are explainable, secure, and fully embedded into everyday processes, not layered on as external add-ons.