Many of our client companies have numerous specialists employed to review financial statements manually. Each report often requires several rounds – under considerable time pressure. So we searched for a solution to reduce this effort without compromising quality. The result is an agent that reviews financial statements using AI, fundamentally changing the process.
Why Reviewing Financial Statements Is So Time-Consuming
A modern financial or business report may appear clear at first glance. However, it hides considerable complexity. Such a report can quickly comprise 50 to 100 pages of tables, charts, free-form text, footnotes, and info boxes. The underlying data comes from a consolidation system and is often exported as an Excel file.
In investor reporting, every detail matters.
- Were all the numbers transferred correctly?
- Is the correct period displayed?
- Are units and notations consistent?
- Was the same value used for a chart as in the table next to it?
Besides obvious errors such as incorrect amounts, rounding differences, and different spellings, currency effects also play a role.
Our Approach: an AI Agent for Investor Reporting
The Review Process
Based on this, we have developed an AI agent that automatically reconciles investor reports with consolidation data. Technically, the solution is based on Azure AI.
The agent first reads the entire report document, typically a PDF. It recognizes numbers in tables, free-form text, charts, or footnotes and analyzes the context: is it revenue, EBIT, margin, percentage change, nine-month value, or year-to-date value? In what language is the text written, and how are units and notations presented?
Then, the agent compares the values from the PDF with the values from the Excel file. It considers not only exact matches but also tolerances, rounding, and data formats.
The Result
After the review, the AI generates another PDF – but with intuitive colored markings directly in the document.
- Numbers that have been clearly assigned and correctly assessed are marked green.
- Positions where a clear assignment isn’t possible, or where the AI is uncertain, are highlighted yellow.
- Red indicates numbers that clearly deviate from the target values.
This way, reporting specialists can immediately see which areas are uncritical and which need closer attention.
Where Does AI Really Create Value for Your Company?
Find out in our free, one-hour AI Discovery Call! Together with one of our AI experts, you will discuss which application fields fit your organization and culture and what implementation effort is realistic.
What an AI-Powered Review of Financial Statements Actually Changes
The most obvious effect is relief from recurring routine tasks. Instead of manually reconciling each individual number in the report with the consolidation file, the agent handles this tedious work. Employees focus only on the areas the AI flags as noteworthy. This saves time, reduces peak stress periods before publication, and lowers the risk of overlooking a critical discrepancy in a long review cycle. At the same time, the review process becomes more transparent. Each marked number is accompanied by a concrete reason: for example, “rounding differs,” “no matching value found in the dataset,” or “deviation from reference value exceeds defined threshold.”
It’s important to note: AI does not replace experts. It accelerates the process and, ideally, also reduces the error rate. The responsibility remains with people, while the AI provides structure, suggestions, and priorities.
Multiformat, Multilingualism, and Extensibility
The developed solution is not limited to a single report layout. It has been designed to recognize numbers in different formats: free-form text, classic tables, diagrams, or info boxes. This makes it suitable for business reports, quarterly reports, presentations for analyst conferences, or fact sheets. Due to its native multilingualism, the agent is also ready for use in an international environment.
The perspectives for further expansion are exciting: while the current focus is on number-based, AI-powered review of financial statements, a next step could be semantic plausibility checks. Then the AI would even recognize whether a textual statement such as “Revenue has increased significantly” actually matches the presented figures.
Automated creation of financial reports in the structure and style of previous reports, and AI-assisted creation of management summaries, are also conceivable. In this case, the AI would create a short summary for the management board or supervisory board based on the previously reviewed numbers.
Who benefits from an AI-driven analysis of financial statements?
This approach is particularly interesting for companies that consolidate multiple subsidiaries, create complex investor reports in several languages, and already invest considerable qualified capacity in manually reviewing their financial communications. Wherever financial communication and controlling are closely linked, and reporting errors are not an option, our agent creates clear added value.
Conclusion: An actual lever for quality and efficiency
AI-powered financial statement review is not an abstract promise for the future but a concrete approach to modernizing reporting processes. It significantly reduces manual review effort, increases transparency, improves report safeguarding, and relieves your experts of repetitive tasks. At the same time, your specialists retain control over content and decisions. The AI assists, organizes, and highlights – the assessment and final approval remain with people.
If you would like to explore how such an agent can be integrated into your reporting and investor reporting, please get in touch with us! Together, we analyze your processes and determine where an AI-powered review of financial statements can create the greatest benefit.
Frequently Asked Questions About AI-Powered Review of Financial Statements
Our AI agent first analyzes the entire report. In doing so, it recognizes not only individual numbers but also their context – for example, KPI, period, unit, or percentage change. It then searches in the underlying consolidation data for the matching reference value. It also accounts for rounding, tolerances, and different data formats. The result is an annotated PDF that highlights correct, uncertain, and deviating values.
The agent can highlight deviations between the numbers in the report and the stored reference data. These include incorrect amounts, inappropriate periods, rounding differences, and different units or notations. Numbers for which a clear assignment is not possible can also be marked for further review.
Yes. Our agent is designed to recognize numbers from different areas of a document. These include classic tables as well as free-form text, footnotes, diagrams, and info boxes. This does not limit the approach to a specific report layout.
Our agent needs the report to be reviewed, typically as a PDF, and the underlying reference or consolidation data. For example, you can provide this as an Excel file. The agent maps the numbers from the report to the corresponding target values from this data.
No. The AI supports specialists in reconciling numbers and helps prioritize suspicious areas. Experts still provide assessment and final approval of the financial statement. The agent primarily takes over repetitive checking tasks and provides hints for further review.
The AI supports the review process, but it is not the sole decision-making authority. It makes clear matches, uncertainties, and deviations visible so specialists can evaluate them specifically. In particular, with regard to financially relevant AI applications, the human-in-the-loop approach is essential.
The main advantage lies in relieving the burden of recurring number checks. Instead of manually reconciling entries page by page with the consolidation data, specialists can focus on the anomalies flagged by the AI. This can focus the review process, increase transparency, and reduce the risk of overlooking discrepancies in extensive reports.
Yes, automated report creation is a possible extension of our agent. It could be conceivable to create reports based on the structure and style of previous publications or to derive management summaries from already reviewed financial data. However, the solution currently focuses on reviewing existing reports.
In addition to the technical quality of the reconciliation, reliable data sources, traceable results, and clearly defined responsibilities are particularly important. The AI should support expert control, not replace it. In a concrete application, you should also examine company-specific requirements for data protection, information security, governance, and regulatory provisions. We will be happy to help you design your solution!
