The challenge
For many organisations, the monthly financial close is a recurring and time-consuming process. While the underlying data and calculations are often relatively straightforward, collecting, validating and reconciling information requires a significant amount of manual effort.
Data is exchanged across multiple departments, several systems are involved, and processes such as reconciliations and intercompany matching still rely heavily on manual work. At the same time, historical adjustments and ongoing reconciliations increase the risk of errors.
The time pressure surrounding the close often leaves little room for where Finance can add the most value. Instead of focusing on deeper analysis or identifying anomalies, much of the effort goes into operational tasks. As a result, strategic insights and improvement initiatives are often pushed aside.
The approach
Artificial Intelligence opens up new opportunities to automate and speed up parts of the monthly close. By taking over repetitive tasks and data analysis, AI allows Finance professionals to focus more on strategic and analytical work.
AI can analyse financial data, transform it automatically, and compare it with historical figures and prior explanations. Modern language models can also generate clear, human-readable commentary to support financial reporting. This makes it possible to create richer, more contextual insights with relatively little effort.
A strong starting point is defining clear objectives. Organisations need to decide what they want to improve, whether that is process efficiency, better decision-making or cost reduction.
The next step is understanding the available data. Which data sources are in place, how are they structured, and what preparation is needed to make them usable for AI? In addition to structured financial data, unstructured sources such as annual reports or narrative explanations also play a role. To use these effectively, it is important to establish clear data governance and ensure information can be interpreted by AI systems. In this context, data quality often matters more than volume.
Finally, organisations determine which AI solution best fits their needs. This could be a standalone application, a solution embedded within an ERP system such as SAP, or a modular platform such as Azure AI. By connecting data sources through platforms like Microsoft Fabric, organisations can bring together different systems and create a flexible foundation for broader AI adoption within Finance.
The result
Using AI in the monthly close allows organisations to significantly speed up processes while improving the quality of financial analysis.
Repetitive tasks are automated, and data is processed faster and more consistently. This creates more time for in-depth analysis, identifying anomalies and generating strategic insights.
Finance teams shift from operational processing to a more advisory role, with AI acting as a digital colleague that analyses data and prepares insights.
The team that made the difference
A multidisciplinary team from KPMG Netherlands supports organisations in applying AI within financial processes. By combining expertise in AI and data with knowledge of Finance transformation and Digital Process Excellence, they help organisations make their finance function more efficient, data-driven and future-ready.
