Digital Financial Reporting

PROLOG (PROgramming in LOGic) is a declarative logic programming language that was created almost 50 years ago.  SWI-Prolog is a robust and free implementation of PROLOG that was created starting in 1987, so it is quite mature.  It can run locally or in the cloud. Note that there is an ISO Standard PROLOG. (i.e. there is a core set of notation which tends to be provided by most Prolog systems and there is now an ISO standard for the language)

Logical Contracts, a company that specializes in PROLOG (Robert Kowalski, a co-designer of PROLOG, is a partner) which has created a PROLOG implementation of XBRL-based report verification. This short video and this short video walk you through how to run the implementation.

I am testing the PROLOG impelementation to be sure it deals with everything in my SBRM PROOF which exercizes all that you would ever see in an XBRL-based financial report.

Here is additional information about PROLOG: 

• Another PROTOTYPE Implementation (by the same person)
• More information about PROLOG

What is of particular interest is the short time that it took to put together the PROLOG implementation.  Now, the person has 30+ years of experience with PROLOG.  The first prototype took about 50 hours with ZERO knowledge of XBRL by the programmer.  The second, which is commercial quality, took about another 30 hours.

More information coming soon, so stay tuned!  If you don't understand why this is a very good thing, you will want to read the four documents here.

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Prolog Business Case

Prolog Tutorial (Video playlist) (SWI Prolog)

Prolog Tutorial (Video) (GNU Prolog)

Comparison of SWI Prolog and RDF

An Introduction to Prolog and RDF

Building Expert Systems in Prolog

PROLOG Cheat Sheet

SWI-Prolog Semantic Web Libraries

Power of Prolog

PROLOG Facts, Rules, Queries

Full Prolog

Auditing is broken. Old school financial reporting processes are inefficient. Accounting is inefficient. Analysis tends to be too manual.  Accounting, reporting, auditing, and analysis needs to move to 21st century techniques.

Old technologies are making it increasingly difficult to keep up with today’s fast paced information exchange. New technologies such as structured information, artificial intelligence, digital distributed ledgers offer significant and compelling opportunities to make accounting, reporting, auditing, and analysis tasks and processes more efficient and effective.  But figuring out how to employ these new technologies and finding people with the necessary skills and experience to analyze systems and fix problems can be challenging. What if there were a standards-based proven best practices method you could use to improve your productivity?

The difference between "custom", a "product", and a "commodity" is explained in this blog post, Properties of Products. In summary,  

• Custom: Means unique to each individual customer. No attempt is made to standardize.
• Product: Found repeatable patterns (clusters), created a product for each pattern. Standards can be used. (As Malcom Gladwell explained in a TED Talk, "The answer is that there is no best pickle or spaghetti sauce.  But there are best clusters of pickles and spaghetti sauces.")
• Commodity: Generalize the product so much that it is fungible, indistinguishable. Standards can be used.

An XBRL-based digital financial report should be indistinguishable in terms of quality.  Are their customers that consciously want low quality?  That would be absurd.  As such, high-quality XBRL-based digital financial reports should be a commodity.

"Custom" is not scalable.  While standard approaches do allow scaling.

Companies should not compete as to whether they can or cannot create high-quality XBRL-based reports.  We need ALL such reports to be of high quailty.  Companies should compete in terms of value added above and beyond fundamental quality, building on top of a rock-solid digital financial report foundation.  Why? So we can reliably and predictably do this:

If XBRL-based reports fundamentally do not work reliably, then such reports are nothing more than an expensive garbage collection scheme.  But what if they do work?

I have put together a series of documents that can jumpstart your understanding of XBRL-based digital financial reporting.  Those documents add up to a total of 161 pages.  If you want to make a small investment, you can learn about XBRL-based digital financial reporting and how those techniques also apply to accounting, auditing, and analysis. Here are the documents in the order that I would suggest that you read them: 

• Understanding Proof: Proves that XBRL-based reports can work and shows you what such a working report looks like and is capable of. (30 pages)
• Understanding Method: Explains, at a high level, a proven, reliable, best practice method for implementing XBRL-based digital financial reporting following the forthcoming OMG Standard Business Report Model (SBRM). (35 pages)
• Understanding Digital: Helps professional accountants get their heads around important aspects of digital related to accounting, reporting, auditing, and analysis. (84 pages)
• Understanding Semantic Shreadsheets: Takes the ideas of XBRL-based financial reports and generalizes them to business reporting. (12 pages)

Not saying that the documents are perfect; they are the best that I can put together currently.  Myself or others will improve this information even more over time.

Want to make a bigger investment? Watch this video playlist and/or read Mastering XBRL-based Digital Financial Reporting.

Forbes published an article, 3 Important Ways Artificial Intelligence Will Transform Your Business And Turbocharge Success, explains that organizations are leveraging AI in three ways: 

• Creating more intelligent products;
• Offering a more intelligent service; (another Forbes article breaks this down)
  • Delivering highly personalized offerings
  • Giving customers more value
  • Predicting customers needs
• Improving internal business processes

These are not mutually exclusive.

It appears that this resource provides a means of extracting information from an XBRL instance and converting it to R.  Not totally sure about that, but that is what this appears to be.  See RCPP. See this on GitHub. See this also.

It appears to be the case that this resource sufferes from the same fundamental problem of Google's BigQuery repository of SEC public company information.