How can XBRL and AI work together?
XBRL is now the standard for financial reporting, providing machine-readable data with vastly improved accuracy and quality to regulators worldwide. With significant amounts of structured data increasingly available, how can AI be used to turn that information into insight?
While some AI programmes are designed to be able to deal with data that is unstructured, using structured data allows AI tools to benefit from higher quality, richer data. This week Visma demonstrated a few simple steps to help AI tools make the most of structured data.
Structured data is particularly useful because each data point contains more than simply the primary value. Instead, a data value is grouped with other important details, like what is being reported, who reported it, and which time period is being reported about. Together, this forms a data point.
In order to apply AI tools and techniques to this kind of data it needs to be filtered for the relevant details, removing unwanted fields, and flattened into a structure that works with AI algorithms.
This means that high quality, structured XBRL data can be retained, while powerful AI tools can use it to drive analysis.
With many AI projects struggling due to a lack of good quality data, the increasing prevalence of structured XBRL data provides a rich mine for training AI and Machine Learning tools.
Read more here.
