The layout of the tree structure of the ESMA taxonomy might man-oeuvre their way faster, but it is still a tedious process that can take them several days to complete.
Automated iXBRL tagging with IRIS CARBON®’s machine learning algorithms
Drawing from IRIS’ deep expertise in XBRL/iXBRL across 32 countries, IRIS CARBON has leveraged machine learning algorithms to automate a large part of the tagging process of Primary Financial Statements of annual reports. This will help issuers immediately meet the first phase of their ESEF compliance reporting requirements.
Our algorithms are trained across multiple languages, including English, French, and Dutch; and are being trained across more EU languages as well. Using these algorithms, IRIS CARBON automatically tags all elements of the primary Financial Statements where it finds a 100% match in the taxonomy. The algorithm also throws up suggestions when not sure of the tag to select. If our algorithms do not find a suitable taxonomy element match or suggestions for any item in the financial statement, they leave such items in the report untagged. For these untagged elements, users need to follow the manual tag selection process as described in the previous section. Rest assured, we make no compromises on the quality of tagging – whatever gets tagged is 100% accurate.
Based on these advanced capabilities in the IRIS CARBON solution, issuers in the EU can more confidently choose to do their iXBRL tagging on their own, and use our expert services for iXBRL conversion services only where required.
Benefit 1 – Save Substantial Time and Cost:
Our advanced algorithms and intuitive tagging approaches save you significant time (with up to 95% of the Primary Financial Statements getting auto-tagged even for first-time filers). Not only do you save many hours, if not days, of your valuable time, you also save money, and simplify your iXBRL reporting process.
Benefit 2 – Greater Accuracy of Data:
iXBRL implementations across the world have ensured the standardization of Financial Reports. However, this still does not address another fundamental challenge of tagging financial information: the data might be structured but is the tagging accurate?
Our platform, for the ESMA iXBRL Mandate, has a tagging accuracy of 100%. Elements that are not tagged are posed as a suggestion.
Given the complexity of today’s regulatory environment, intelligent technology implemented correctly can ensure consistency, lower the probability of human error, and significantly reduce an organization’s time and effort spent in the regulatory compliance process. Get on board now for the ESMA iXBRL mandate, and change the way you tag for good!
