In this article, we’ll explore how AI advancements can take the load of corporate reporting so finance teams can meet the influx of financial, statutory, and management reporting needs— without straining their systems.
Corporate reporting is part science, part art, part story. Managing accurate disclosures, crafting compelling reporting narratives, and powerfully visualizing performance trends — these are the table stakes of corporate reporting processes. And they’re about to be challenged by a flurry of new corporate reporting requirements, like carbon emissions disclosures, CSRD, BEPS Pillar 2, and CbCr.
Mix in the C-suite demands for real-time KPI updates, tumultuous economic landscapes, and rising costs, and the pressure on reporting processes—and the teams responsible for them—has reached titanic depths.
The good news? Artificial intelligence (AI) has matured just in time to give finance teams a helping hand. In this article, we’ll explore how AI advancements can take the load of corporate reporting so finance teams can meet the influx of financial, statutory, and management reporting needs— without straining their systems.
What you’ll learn:
- Quick recap: How AI works
- The role of GenAI in narrative reporting
- The role of AI in disclosure management
- Risks and challenges of AI-driven reporting
- Best practices for integrating AI
- Get prepared for a future of AI in corporate reporting
Quick recap: How AI works
AI is a computing process that uses algorithms and machine learning models to give data-based tasks sophisticated levels of automation. There are a few categories of AI, including:
- Generative AI (GenAI): GenAI uses a combination of statistical algorithms, machine learning, and learning models, including large language models and natural language processing, to create complex and creative content, like images, music, videos, and text, based on the data they’re trained on.
- Machine learning: Algorithms that use patterns it finds in data to make predictions, classify trends, and generate new information.
For finance, AI is used in the following ways:
- Predictive analytics: Predictive analytics makes predictions by identifying patterns in large datasets and measuring the likelihood that those patterns will reoccur. Finance can leverage predictive analytics for planning and forecasting.
- Data discovery: Also known as Analytical AI, Objective AI can automatically identify patterns in large volumes of structured and unstructured data faster and more efficiently than humans. Finance teams can use it to identify performance drivers, which is especially useful when changes in performance aren’t immediately evident.
- Data management: Accuracy is everything for finance teams tasked with signing off on the proprietary information disclosed within statutory reports. However, data errors can easily make their way into reporting systems and final reports. Finance can use AI to flag disturbances in data for review.
- Report design: GenAI is skilled at understanding language, creating content, and managing large volumes of data. Finance teams can take advantage of GenAI when they need to:
1. Input data into new reports or update data in existing reports
2. visually present data trends
3. describe performance in reports
GenAI can be useful in assisting dashboarding, narrative creation, data visualization, and analytics tasks.
How GenAI can augment corporate reporting
GenAI's power is its ability to create content, including videos, text, and images. This makes it an exceptional tool for aiding corporate reporting tasks, such as creating the annual report, assembling sustainability disclosure, and generating data-driven insights.
Because genAI can make quick sense of information in large databases and then translate it into narratives and visualizations, it can do the heavy lifting for finance teams of report creation when integrated into their CPM solutions.
During narration creation, GenAI can:
- Suggest improvements to existing text
- Create text based on a prompt from the user
- Craft text, like reporting narrative, based on the data trends present in the report
- Analyze additional files and sources of information to inform its textual recommendations
During visualization, Gen AI can:
- Turn a Microsoft Word document into presentation slides
- Suggest visual enhancements to design, formatting, and layout
- Take a performance trend found within a dataset and turn it into a graph or chart
