Emergence of AI in XBRL Tagging

What is XBRL Tagging?

XBRL tagging is the process of assigning unique identifiers to various elements of financial or sustainability data—such as revenue, expenses, emissions, and head count. These tags allow software to easily recognise facts and figures, which then can be reliably compared, and analyzed across different platforms and systems.

 

The rapid emergence of AI in XBRL tagging

In the rapidly evolving landscape of financial reporting, eXtensible Business Reporting Language (XBRL) has become a transformative technology, reshaping how financial information is communicated. At the core of XBRL's impact is the concept of tagging which plays a vital role in making financial and sustainability data more accessible, more accurate, and more actionable. AI brings a new level of automation and intelligence to the tagging process, lessening the burden on filers and further revolutionizing the efficiency and transparency of financial and sustainability reporting.

AI is increasingly playing a pivotal role in enhancing XBRL tagging processes. Here’s how AI contributes:

How XBRL Tagging solves customer challenges

XBRL tagging addresses several common challenges faced by customers in financial reporting:

1. Inconsistent reporting standards: Different jurisdictions and industries often have varying financial reporting standards. XBRL tagging standardizes financial data, making it comparable and consistent, thus facilitating easier cross-border and inter-industry analysis.
2. Data complexity and volume: Handling large volumes of complicated financial data can be overwhelming. XBRL tagging organizes data efficiently, turning complexity into comprehensible, machine-readable data that stakeholders can easily analyze.
3. Error-prone manual processes: Manual entry and reporting processes are susceptible to errors. Automated XBRL tagging reduces human error, ensuring accuracy and reliability in financial disclosures.
4. Time-consuming compliance checks: Compliance with ever-evolving regulatory standards can be time-intensive. XBRL tagging automates compliance processes, significantly reducing the time and resources needed for validation and reporting.
5. Limited transparency and accessibility: Traditional financial reports often lack the transparency required by investors and regulators. XBRL enhances data transparency and accessibility, providing stakeholders with detailed and insightful financial information in a timely manner.

 

How XBRL Tagging works

The process of XBRL tagging involves several key steps:

1. Taxonomy selection: Choose an appropriate taxonomy that fits the reporting requirements. Taxonomies act as dictionaries of financial or sustainability reporting concepts tailored to specific industries or jurisdictions.
2. Data mapping: Identify and map the relevant financial data points to the corresponding elements in the chosen taxonomy.
3. Tagging: Attach tags to each data point using standardized codes, ensuring each element in the financial report is labeled for machine-readability.
4. Validation and review: Validate the tagged data for accuracy and compliance with regulatory standards, ensuring the XBRL document is correct and complete.