AI in financial reporting

The financial industry as a whole is being revolutionized, and it’s forcing financial planning and analysis (FP&A) teams to reimagine the financial reporting process.

Using AI for financial reporting is no longer a suggestion. It’s an imperative for companies trying to stay ahead of the competition. A KPMG study found that nearly 72% of companies surveyed are piloting or using AI in financial reporting, and they expect that number to rise to 99% in the next year.

Financial reporting is a monotonous task for finance teams, requiring them to deal with Security and Exchange Commission (SEC) filings, changing regulatory requirements and environmental, social and governance (ESG) reporting. Collecting financial data for each of these filings is time-consuming and expensive. This is where AI for finance steps in—accelerating financial reporting, sharpening insights and strengthening the overall decision-making process.

AI is being used in financial reporting beyond just task automation and data collection use cases. Technology like generative AI and predictive analytics is being used to create AI-driven forecasts and individualized reports tailored to specific stakeholders.

Some of the key ways AI is being used in financial reporting:

• Automation: Applying financial reporting automation tools allows finance teams to set up rules and automate processes that can run in real-time. Modern AI-powered planning solutions can integrate with existing spreadsheets and systems, including enterprise resource planning (ERP) software. The resulting automations can reduce operational costs and redeploy the workforce for strategy and financial analysis.
• Audits: Implementing AI technology for audits can help finance teams with data analysis and quality management. Audit processes can be tedious, as analysts must review large volumes of data and maintain audit trails. This process is where AI tools can quickly analyze vast amounts of data and uncover insights that traditional analysis might miss.
• Compliance: Adding gen AI for finance capabilities to finance functions is especially beneficial for compliance. Financial institutions must navigate a complex regulatory environment, including the Anti-Money Laundering Act (AMLA) and the Bank Secrecy Act (BSA). Generative AI, specifically large language models (LLMs), can automate compliance processes and detect disparities.
• Data analysis: Predictive analytics and machine learning (ML) are transforming how organizations analyze datasets and spot trends. AI models can handle data extraction and data validation, analyze patterns, and make data-driven decisions—all in real-time. These capabilities make finance teams more efficient by detecting unusual patterns in the data long before an issue arises, leading to stronger data integrity and proactivity.
• Fraud prevention: Enlisting AI systems for financial reporting helps organizations create strong risk management practices and personalized fraud prevention. Machine learning tools monitor an organization’s data in real-time, and a properly trained tool can recognize potential security breaches or provide advanced warnings when fraud is detected.

Implementing AI in finance is an iterative process that will require fine-tuning and collaboration from within the organization. The process for implementing AI into financial reporting will differ by organization size and industry. However, to get started, a standard set of steps can be followed.

Before bringing AI into the organization, finance leaders should assess how important technology is for the business or the financial reporting function. Examine current financial processes and identify areas of the financial reporting role that might be automated or improved.

Consult with finance leaders and stakeholders across the organization to understand how they see the business improving and evolving with AI capabilities.

AI tools for financial services are not one-size-fits-all. They will require finance teams to think deeply about what business goals they are trying to accomplish with the technology.

The chosen AI tool should align with the reporting needs. For example, an organization might consider natural language processing (NLP) for analyzing large datasets or predictive analytics for financial forecasting.

Agentic AI is another technology option that organizations might consider for more advanced, intuitive predictive insights.

Positive results from AI tools stem from strong, accurate data at the base.

AI systems are fed historical data on income statements, cash flow statements, balance sheets and other financial information from within the organization. Organizations must feed AI tools high-quality data and establish governance policies to ensure accuracy and ethical use.

Finance team executives and managers should properly prepare staff for the implementation of AI capabilities. The technology can be overwhelming, which is why leaders should help employees understand how AI tools can enhance decision-making and improve the accuracy of their outputs.        

Implementation might also require employee upskilling, depending on the software or tools being implemented. The AI system is not meant to replace human judgment and should be communicated as an enhancement to existing workflows.

‘Set it and forget it’ is not the right mindset for AI tools. Finance teams need to continuously monitor and regularly update the tools to reflect changes to the business—both internal and external.

If business goals change and key financial metrics shift, it’s crucial to update and evaluate AI tools, helping ensure that they continue to have a positive impact on the organization.

Companies that implement AI in their financial reporting process will enjoy many benefits, including the following.

AI-driven financial reporting analyzes historical transactions, market signals and operational data to model likely outcomes. By having a central dashboard that tracks real-time data, finance teams can forecast revenue, expenses and cash flow with greater confidence.

AI tools help finance leaders understand the drivers behind change—what is moving results and why. The technology tests scenarios, highlights sensitivities and quantifies potential impacts for the finance department and other business units.

In the long term, AI tools can improve planning cycles, align stakeholders and reduce the chance for surprises.

By continuously monitoring transactions, controls and external signals, AI tools provide insights into risk and help mitigate potential issues.

AI tools help finance teams prioritize alerts based on severity and potential impact, reducing noise and focusing attention on the issues that matter most. With the enhanced visibility, organizations can strengthen governance and mitigate exposure before issues escalate and affect financial statements.

AI tools eliminate the need for manual data entry, saving finance teams time and money.

The tools can also sift through structured and unstructured data to create a single view of data across an entire enterprise. Machine learning (ML) algorithms learn from historical data and detect anomalies and inconsistencies in real-time, helping mitigate problems before they occur.

Separately, AI tools automate account reconciliation by instantly matching transactions against company records, bringing up discrepancies immediately rather than several months later.

AI tools are fueled with data, and they drive decisions based on facts and key metrics.

AI tools can unify data sources and deliver timely, relevant analysis to finance teams. The AI technology cleans, classifies and enriches data so finance teams work from a consistent foundation. Interactive dashboards and natural language summaries even make insights accessible to nontechnical stakeholders and employees.

Finance teams can collaborate around a single version of truth, reducing debate and accelerating approvals. This approach leads to greater transparency across functions and stronger alignment with strategy.

Deploying AI in financial reporting can be daunting, with many factors to consider. These are some of the best practices companies should consider when implementing AI into their financial reporting function.

Create an AI framework

• Start by defining the scope of AI use within the financial reporting function.
• Establish governance structures at the very start of implementation to make clear who owns AI decisions and who has oversight.
• Define data standards and quality requirements that the AI system must meet.
• Set measurable performance benchmarks to compare against AI outputs.
• Establish a review cadence to evaluate and update the framework as technology evolves.

Integrate AI training and enablement

• Assess the current skill gaps within the finance team before introducing AI tools.
• Develop role-specific training so analysts, managers and executives each understand how AI tools will impact their role.
• Use real financial data to create hands-on AI learning opportunities for employees in a controlled setting.
• Create an internal resource hub where teams can access guides, updates and best practices.
• Designate AI leaders within the finance department to support peer learning.

Drive the importance of ethical AI use

• Define what responsible AI means for the organization in the context of financial reporting, including fairness, transparency and accountability.
• Require final sign-off and review from human employees on all AI-generated financial outputs.
• Establish protocols for identifying and modifying AI bias in data models.
• Align AI use policies with existing regulations and compliance requirements.
• Create a straightforward protocol for reporting ethical concerns or model failures.

Create best practices for AI adoption

• Start small with a pilot program on a contained reporting function before scaling.
• Document lessons learned from the initial pilot phase and use them to refine the adoption roadmap.
• Set key performance indicators (KPIs) to establish a baseline for measuring how AI tools are impacting the team.
• Build change management strategies to address employee resistance and encourage AI adoption.
• Revisit the best practices regularly as AI technology evolves and business needs shift.

AI technology is no longer a future consideration for finance teams—it’s an operational reality. Organizations across industries are accelerating AI adoption, and financial reporting is one of the functions feeling the shift most acutely.

AI-powered tools are moving well beyond task automation. They analyze entire datasets, unify FP&A teams and streamline planning, budgeting and forecasting. The result is a reporting function that is faster, more accurate and better positioned to inform real-time decision-making.

However, transformation comes with real challenges. Data quality, integration hurdles and regulatory compliance around AI-generated reports remain significant obstacles. Over-reliance on AI outputs without sufficient human review is also a growing concern for finance leaders. That’s why governance matters from the start. Organizations that put guardrails and protocols in place for AI use will be better equipped to scale responsibly.

Finance leaders must emphasize that professionals aren’t being replaced by these tools; they’re being repositioned to focus on interpretation, strategy and oversight. Finance teams that embrace this shift now and build solid foundations will be best positioned to lead as AI capabilities evolve.