What to look out for when using AI in financial reporting

Artificial intelligence is changing the way finance teams can approach financial reporting. From automating repetitive tasks to analyzing financial statements, AI-powered tools promise faster and more efficient financial reporting processes. The use of AI in finance to create common reports like balance sheets, cash flow statements or for more specific tasks like performance analysis dashboards can offer significant benefits. Yet along with the benefits of AI come some known issues.

Hallucinations, inconsistency in outputs, repeatability issues and variations between results of different team members can all impact accuracy and data trust. The goal here isn’t to spread fear or discourage innovation rather it’s to highlight the common watch outs so finance teams can adopt AI technology with confidence and strong governance.

Understanding hallucinations in AI-generated financial reports

One of the recurrent risks of generative AI is the possibility of hallucinations. In the AI context this means the system produces information that sounds correct but is inaccurate or completely fabricated. In everyday use, this can be managed but in financial reporting, inaccuracy can overstate or diminish business performance. For example, a hallucinated figure in a balance sheet can distort the company’s financial position.

Finance teams must treat AI-generated numbers as provisional until they are validated against the source financial data. This means maintaining rigorous checks, verifying figures with ERP systems or setting up alerts to check every AI-powered insight. AI can accelerate financial analysis but currently it is not a substitute for professional accountability.

Addressing inconsistency in AI outputs

AI models, especially generative AI tools, can sometimes deliver different answers to the same question. This variability can stem from randomness in algorithms, changes in data inputs or model updates by the AI provider. While such variation might be acceptable for exploratory market trend analysis, it poses problems when producing audited financial statements or doing financial planning.

To counteract inconsistency, finance teams should aim to standardize their AI usage as much as possible. This includes working with consistent AI settings, locking model versions where feasible and aligning parameters across teams and processes. Consistency is key in ensuring financial information remains reliable, enabling finance leaders to make informed decisions with confidence.

Managing differences between team member results

When AI is embedded within financial software tools different team members may unintentionally receive different results for similar queries. This can happen due to variations in prompt wording, differences in access permissions or even the timing of when a request is made. In financial reporting processes, such discrepancies can create confusion and raise concerns during the final reconciliations.

The best way to prevent this is by fostering a culture of shared AI practices. Finance teams should agree on standard approaches to using AI, including preferred prompt structures, approved workflows and consistent validation steps. Regular training sessions can help align team members, ensuring the entire finance function benefits from the same level of AI capability and accuracy.

Ensuring repeatability in AI-driven financial reporting

Repeatability is the crux of credible financial reporting. Whether preparing monthly management reports or generating annual cash flow statements, the ability to produce consistent results is essential for maintaining trust with stakeholders and complying with regulatory requirements.

If AI systems cannot reproduce the same output under the same conditions, the credibility of the financial reporting process is at risk. Your finance team should document your AI usage carefully and capture the prompts, data sources, algorithms and parameters involved in producing each output. Where possible, you should integrate AI into automated reporting that can be used in the same way every time, ensuring the reporting process is not only faster but also reliable.

Advancing as a team through continuous AI improvement

The use of AI in finance is evolving rapidly. New algorithms, better predictive analytics and improved machine learning models are emerging regularly. For finance teams, this means the way you implement AI today will likely look different in a year’s time. However, it’s essential that improvements benefit the whole team, not just individuals experimenting in isolation.

Regular knowledge-sharing sessions can help ensure that new use cases, lessons learned and workflow enhancements are communicated across the team. Finance leaders should embed these improvements into documented standard operating procedures so that every member benefits from advancements in AI capabilities. When performance metrics improve—whether through faster data entry, reduced forecasting errors or more accurate financial performance dashboards—these wins should be shared and replicated across the department.

Risk benefit analysis

AI brings incredible benefits to financial management. It can analyze large datasets, spot anomalies in financial information and generate customized reports across multiple entities. Predictive analytics can also enhance financial forecasting, helping CFOs and finance teams anticipate risks and opportunities more effectively. AI tools can also automate time-consuming, repetitive tasks such as data reconciliation, freeing up finance professionals to focus on higher-value activities.

However, these benefits must be balanced with a clear-eyed view of the risks. AI outputs must meet regulatory requirements, pass validation checks and be documented. Data quality is critical. As we know flawed data inputs will lead to flawed outputs, no matter how advanced the AI system. Maintaining strong governance over AI-driven financial reporting ensures the technology serves as a reliable partner for finance teams rather than a source of uncertainty.

Where AI delivers the greatest value in finance

When implemented specifically for key industries and needs, AI can enhance many aspects of financial reporting and planning. It can provide AI-powered dashboards to visualize financial health and build regular cash flow statements. In compliance-heavy industries, AI technology can help ensure that regulatory reporting is accurate and on time, flagging potential risks before they become issues. By automating repetitive elements of month end and the audit process, it can speed up financial closing cycles and free up finance teams to share performance information. with the whole business and help people understand and act on the results.

Implementing AI successfully in financial reporting

Successful implementation of AI in finance starts with your most pressing needs. Get your finance team to identify the most valuable use cases for AI now and then again in 6 months. If you need better budgeting and forecasting then select the tool to match. Many tech providers are allowing trials, or you can buy a few licences to run pilot projects so you can validate the accuracy before scaling AI capabilities across the financial reporting process.

Integration with existing systems is important and the data preparation is paramount. Training and documentation are also important. Finance professionals must understand not only how to use AI tools but also how to interpret AI-generated outputs critically. Continuous monitoring and refinement of AI models will help maintain accuracy, reliability and relevance over time.

As AI capabilities expand, finance leaders can continue to expect more accurate predictive analytics and faster turnaround times for financial reporting. Natural language processing makes it easier to query financial data directly and improved algorithms will refine risk assessment and financial forecasting models.

We predict most finance teams will eventually embrace the power of AI but will need to keep strong controls in place. AI should be used in conjunction with human expertise by blending the speed and scale of AI systems with the judgment and experience of finance professionals. Adopting AI in financial reporting is more than choosing the new technology it’s about building confidence in the results it produces.