Get More Out of Your Monthly Closing with AI

In most cases financial monthly closure processes are not necessarily complex in terms of content or data, but they often cost a significant amount of valuable time. Data is exchanged between various people and/or departments, there are often multiple systems involved, there is a relatively high risk of error due to direct reconciliations and historical changes and a lot of human intervention is required in the intercompany processes. The time-consuming nature of this way of working often leaves little or no time for thorough analyses of results or close monitoring of anomalies. Let alone for well-considered strategies to improve the results or processes. 

By delegating part of these tasks to Artificial Intelligence, Finance professionals can focus much better on where they can truly make a difference: strategic and analytical procedures to identify opportunities for improvement. AI is particularly well-suited for analysing financial data, automatically transforming it, and comparing it to both historical data and commentary on that historic data. With the right prompts, current language models can even generate plain text to further clarify the reports in no time. As a result, comprehensive and customized data insights are generated at the touch of a button. Leveraging both internal and external data sources.

The first step is establishing what you want to achieve and setting clear goals. Are you aiming to run a better business, cut costs, improve decision making, or do you have more specific goals in mind? In other words: define the optimal outcome. The second step is to assess the data that you require for achieving your goal. What data sources do you have? How are they structured? What classifications are used? And how can you have it AI-ready? Most often it includes, in addition to financial data, also non-financial data, such as annual reports. Measuring quality on such unstructured data is difficult, therefore you need to set the right rules around the use and ensure it is readable for the AI-system. It's also important to realize that better data is more valuable than simply having more data, if only to save costs and maintain an overview. The third step is the selection of the AI solution or agent, which again depends on your situation. It can be developed stand-alone (out-of-the-box) as a best-of-breed AI solution for the task you want to solve, embedded in the original source of records, such as ERP systems like SAP, but also within a more modular development platform, such as Azure AI. This enables you to connect multiple data sources and in sourcing a wide variety of use cases, while staying in control of your IT landscape. If you are dealing with multiple sources, you'll need a dedicated platform to orchestrate and connect different data sources, like Microsoft Fabric – or any other qualified AI layer.