Digital Financial Reporting

NFT trade volume is up: (interesting embedded chart) 

You can get more information here.

Auditchain is creating Pacioli which is a PROLOG based logic/rules/reasoning engine specifically tuned for scrutinizing and otherwise working with XBRL-based financial reports.  One interface for that software application is this:

Pacioli Power User Tool (To log in, click on the colorful icon on the right.)

See also these training resources. 

• DOW 30 FAC Verification
• States (see documentation)
• Method (see documentation)
• ACME example (very large report, lots of rules)
• Microsoft Financial Report Knowledge Graph
• XBRL-based Financial Reports Submitted to SEC using US GAAP, Fundamental Accounting Concept Relations Verification
• Adding and manipulating information
• Importing report information (Example import files you can use: Accounting Equation, SFAC6, Proof)
• XBRL Technicalities
• XULE and PROLOG (Get more information about XULE here)

More information coming soon.  Keep watchining this page.

For viewing XBRL-based financial reports, you might want to consider trying Pesseract.

Why PROLOG for processing XBRL-based financial reports? As I have pointed out, XBRL formula processors have specific defeciencies in functionality. They simply don't have the necessary capabilities.  There are other alternatives to processing knowledge graphs. Fundamentally, XBRL-based financial reports are knowledge graphs.

PROLOG fits particularly well this challenge, because it provides "out of the box" (in addition to the ISO PROLOG standard core) a number of important key features: 

• Relational paradigm addresses tabular data (e.g. RDBMS) while optimised support for recursive code addresses tree and graph data (e.g. graph DBs, RDF) (note that at its foundation, XBRL also follows the relational paradigm)
• Basic logic engine already built-in.
• Extensive meta programming capabilities, handy for formula evaluation.
• Flexible parser, facilitating friendlier syntaxes.
• Builtin search and tree matching.
• Very efficient C-based engine, with RAM usage an order of magnitude under the alternatives.
• Vast open source API libraries, including XML and semantic web processing processing.
• Mature implementation, consolidating over 3 decades of optimisation and refinement in the fields of logic programming and cognitive artificial intelligence.
• Recent features for smoothier integration with other languages and to build Domain Specific Languages.

You can find a more comprehensive list of PROLOG advantages here, and a related business view perspective here.

XBRL International is publishing a list of XBRL-based reports that have been submitted to the ESMA.

This is an excellent resource: The Knowledge Graph Cookbook: Recipes that Work. Well worth reading. You can download that PDF or purchase a printed copy on Amazon. (I put the PDF on my web site here.)

The book is described as follows:

The Knowledge Graph Cookbook explains why your organization should invest in the development of knowledge graphs, and most importantly, what recipes exist for developing and integrating them in an efficient, successful and sustainable way. The Knowledge Graph Cookbook will be a valuable resource for practitioners and decision-makers to take advantage of the many benefits of this methodology, especially the positive impact it has on the implementation of AI strategies. Learn more about this innovative approach called semantic AI. The Knowledge Graph Cookbook is based on more than 20 years of experience that both authors, Andreas Blumauer and Helmut Nagy, have gathered in the field of semantic technologies, text mining, data management, knowledge management and AI for business use cases.

Particularly interesting if you have read this article about financial report knowledge graphs and this article that explains that XBRL is effectively a knowledge graph.

While this book focuses on RDF+OWL+SHACL and graph databases (labeled property graphs, LPG); there is a third kind of primary problem solving logic paradigms: logic programming. I do agree with the authors that standards will matter and a standard approach that all three problem solving logic paradigms will drift to.

XBRL is mentioned as being a Domain Knowledge Graph on page 108 of The Knowledge Graph Cookbook: Recipes that Work:

##########################

What is a Knowledge Graph?

Validating RDF Data

Introduction to Graph Databases (Video by Tiger Graph)

Comparing Relational, NOSQL, and Graph Databases (Video by Tiger Graph)

XBRL US created something which they call Xule.  Xule is described as follows:

XULE is an expression syntax that allows the querying of XBRL reports and taxonomies using a XULE processor. The XULE syntax has two distinct components. The first is factset selection and the second is taxonomy navigation.

Here is documentation.

It seems that XBRL US Data Quality Committee rules are written in Xule.