XBRL (Extensible Business Reporting Language) is an extensible financial reporting language. It was created to unify and simplify the language of financial reporting, for the exchange of information, financial and business data. It accelerates the reporting and analysis of this information.
According to the Regulation of the European Commission, starting from 01.01.2020, entities issuing securities admitted to trading on regulated markets in the European Union are obliged to prepare annual reports in the uniform European reporting format “ESEF” (European Single Electronic Format).
All consolidated annual financial statements are to be prepared in accordance with International Financial Reporting Standards (IFRS) and tagged using the XBRL markup language.
XBRL is based on XML, so it is not tied to any hardware or software platform. XBRL is a technology for marking (tagging) information that allows it to be identified and described within the financial statements of a given entity. XBRL tags are included in the XHTML document, i.e. in the format of consolidated financial statements, which is readable by any web browser.
The basic feature of the XBRL language is the unambiguous identification of the semantics of the elements of the report, by referring each of them to a certain dictionary, called XBRL taxonomy (XBRL Taxonomy). Taxonomy creates a set of information that defines and classifies concepts that appear in financial statements. It also allows you to hierarchize structures, as well as their mutual dependencies. It is a kind of dictionary that allows assigning a selected item, an amount in the financial statements of “significance” in the form of the value described in accounting standards (IFRSs), regardless of the name the inventor uses in relation to a given item in his statements. Each element, each number of financial statements is associated with this dictionary, XBRL taxonomy – it is assigned attributes such as m.in currency, level of rounding or the period to which it relates. Taxonomy is therefore a set of knowledge about a set of economic indicators that should be included in financial statements. In addition to the definition of parameters, the specification describes the rules for their validation and the rules for presentation on official statements.
Each of the reports has a unified structure containing information on economic indicators such as m.in revenues, EBITA, indicators related to the company’s liquidity.
The document that contains the financial information is called an XBRL Instance. It is an electronic document. The financial statements contained in this document can have any content, structure and order, because all financial facts , marking by means of identifiers (tags) has unambiguously and clearly defined and marked characteristics, e.g. amount, date, currency.
Inline XBRL (iXBRL) is a special version of the XBRL standard that combines the advantages of HTML and XBRL. The XBRL standard itself is code that is not readable without the use of appropriate software, but thanks to this combination, reports can be read using web browsers. Inline XBRL is used all over the world, also by European Union countries, including Poland.
One of the most interesting solutions that supports companies in the implementation of financial reporting is the solution of the company BFT24.COM.
The BFT platform allows you to create financial reports that provide comprehensive information about companies, including detailed information on published reports from the American market (over 20,000 unique indicators per report). The solution allows you to import data, XBRL reports (ESEF) to the BFT24 service, where the information is presented in a legible way in the form of a table or “tree”.
The innovation of the BFT platform lies not only in the implementation of the new ESEF reporting, but in the introduction of unique mechanisms related to data verification and analysis based on a global set of reports. The BFT platform, unlike existing solutions, verifies and corrects the document taking into account other reports submitted by companies from the same sector (macro). Created indicators based on broadly understood Machine Learning (ML) algorithms, make it possible to identify an area with a high probability of error.
The ML module is integrated into the platform. Thanks to this procedure, system users get a powerful analytical tool. They have the ability to build statistical models directly on the data, without the need to extract it. They receive a platform through which they can also create their own unique indicators.
