XBRL-based Digital Financial Reporting

 In the article, What is a Knowledge Graph? , IBM describes knowledge graphs as such: A knowledge graph, also known as a semantic network, represents a network of real-world entities—i.e. objects, events, situations, or concepts—and illustrates the relationship between them. This information is usually stored in a graph database and visualized as a graph structure , prompting the term knowledge “graph.” Note the highlighted and bold text "...and visualized as a graph structure...".  That is a true statement about the visualization of a knowledge graph.  A graph view is a general way to view a graph of information.  But that "general way" is only "generally useful". You can also view knowledge graphs in specialized ways .  For example, this is a knowledge graph of a financial report: ( click here for a larger view ) That same report might look something like the following in say Neo4j: Both versions of the rendering of a knowledge graph have their uses.  T

Financial reports are knowledge graphs of the logic conveyed by the information within the financial report.  A problem arises when a knowledge graph of a financial report is less capable masquerades as more capable or fully capable knowledge graph of meaning. The Seattle Method provides guidance. The Seattle Method is a proven, good practices, standards-based pragmatic approach to creating provably high quality XBRL-based general purpose digital financial reports when reporting entities are permitted to modify the report model. Financial reports are knowledge graphs.  In the past, these knowledge graphs have only been readable by humans.  Now, using the XBRL global standard, the knowledge graphs can be readable by both humans and machines: financial reports are machine-readable global standard knowledge graphs of XBRL-based information.  The only way this “machine-readable financial report” thing can work is if such reports are trustworthy, interpretable, explainable, and the origin

(UPDATE: President Biden did, in fact, sign the Financial Data and Transparency Act into law on December 23, 2022 as part of the National Defense Authorization Act .  Here is the actual text of the bill that was passed .) In July of 1998 a meeting of the AICPA High Tech Task Force took place in Sedona, Arizona.  The purpose of the meeting was to discuss an idea that I had to represent financial reports in machine readable form using the Extensible Markup Language (XML) which was published by the W3C earlier that year. At the meeting were (pictured below in order) John Woodburn (The Woodburn Group), Dianne Spencer (Deloitte & Touche LLP), Charles Hoffman (Knight, Vale and Gregory), Mike Harnish (Dickinson Wright et al), Wayne Harding (Great Plains Software), Chris Reimel (N.J. Department of Labor), Barbara Vigilante (AICPA), and Karen Waller (AICPA). As a result of that meeting, the industry standard Extensible Business Reporting Language (XBRL) was created and XBRL International

The document,  Good Practices Model for Implementing XBRL-based Reporting System , documents a practical, good practices-based guidance for implementing an XBRL-based reporting system where those reporting within the system are permitted to modify the report model and the need for quality is high. The good practices-based guidance was gathered over probably 20 years and builds on the foundation set by the U.S. Securities and Exchange Commission (SEC) and European Single Market Authority (ESMA) which collect financial information of public/listed companies using XBRL. A very basic working prototype is provided which provides all the details necessary to implement your own XBRL-based reporting system.  You can get to that working prototype here . This graphic compares the good practices model with the SEC and ESMA XBRL-based report repositories: Try the good practices model out and see how it works.  Why reinvent the wheel or repeat the mistakes that have been made by others.

The  Financial Data Transparency Act of 2022 is a bill whose purpose is: To amend securities and banking laws to make the information reported to financial regulatory agencies electronically searchable, to further enable the development of regulatory technologies and artificial intelligence applications, to put the United States on a path towards building a comprehensive Standard Business Reporting program to ultimately harmonize and reduce the private sector’s regulatory compliance burden, while enhancing transparency and accountability, and for other purposes. The bill specifically mentions the following U.S. Federal agencies that would likely be impacted: Securities and Exchange Commission Federal Deposit Insurance Corporation Office of the Comptroller of the Currency Bureau of Consumer Financial Protection Federal Reserve National Credit Union Administration Federal Housing Finance Authority The bill is basically getting the federal government to use data standards to create machi

Someone made me aware of ChatGPT a couple of days ago.  That is incredibly interesting and worth checking out. This article,  A Beginner’s Guide to ChatGPT: Understanding What it Is, Why it Matters, and When/Where to Use It , was ultra helpful in figuring out how to use ChatGPT.  This video,  ChatGPT Introduction - What It Is, How to Use It & Why It Matters , is also very useful. Try an experiment.  Enter each these questions into ChatGPT and just watch what happens.  Enter the first question; then the next; then the next. Read the answers you get.  After you do this, try modifying my questions to create your own questions. * * * Can you generate business ideas for a new software service in the financial reporting space related to artificial intelligence? Can you give me more details related to the AI-powered financial reporting tool that helps businesses automatically generated accurate and up-to-date financial reports? What type of AI is best for and AI-powered financial reporti