Workiva has developed a system for XBRL tag outlier detection, utilizing machine learning to identify uncommon tags in financial filings. The method involves receiving XBRL data, training a model, and analyzing documents to pinpoint outlier tags. This innovation aims to enhance data accuracy and compliance. GlobalData’s report on Workiva gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Workiva, Intelligent voice assistant was a key innovation area identified from patents. Workiva's grant share as of January 2024 was 68%. Grant share is based on the ratio of number of grants to total number of patents.
Xbrl tag outlier detection using machine learning
A recently granted patent (Publication Number: US11868400B1) discloses a method implemented by a computer system involving the use of XBRL data records and machine learning models. The method includes receiving a first set of XBRL data records, generating a second set based on a subset of the first set, training a machine learning model using both sets, receiving an XBRL document with assigned tags, and analyzing the document to identify outlier XBRL tags uncommon for corresponding filing information. The generation of the second set involves modifying XBRL tags and filing information to identify outlier tags based on a taxonomy.
Furthermore, the method involves analyzing filing packages, determining classification identifiers, selecting trained machine learning models based on these identifiers, and identifying outlier XBRL tags in assigned tags. The selected model is trained using XBRL data records, and the generation of the second set involves modifying tags and filing information to identify outlier tags based on a taxonomy. The output of the method includes a set of identified outlier XBRL tags, with options for indicating document locations and generating files. This patent showcases a sophisticated approach to analyzing XBRL data using machine learning models to identify outlier tags, potentially improving data accuracy and compliance in financial reporting processes.
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