Digital Financial Reporting

So in prior posts I mentioned computational law and computational audit.

In this post I want to provide an example of computational economics which is another example of a symbolic system.  Here is a definition of computational economics.

I read a book over the weekend, The Deficit Myth, which explains Modern Monetary Theory (MMT).  MMT like other economic theories have models.  Here is one model from MMT: (from here)

(T-G) + (S-I) + (M-X) = 0

I took that MMT model and some other things from the book and represented in in XBRL.  Here is my first draft: 

• Human Readable
• Machine Readable XBRL instance
• Machine Readable XBRL taxonomy
• Machine Readable XBRL Formulas (Rules) (human readable validation result)
• Download files

Again, what I have right now is just a draft.  I want to do this for, say, multiple different governments like the US, UK, Japan.

What if these models and data were provided in machine readable form? What if the information format was standardized rather than being provided in Excel or CSV?  What if the rules where not embedded in Excel, but rather publicly available and usable across models?

I am going to build out my MMT model and put in real data in order to check out MMT to see if it makes sense.  So, stay tuned.

Stanford University has a unique undergraduate or graduate major offering called the Symbolic Systems Program.

So, what is a symbolic system? Per the associate director of the program when interviewed by The Stanford Daily:

“[The major is] a combination of studying the human mind … and the intelligence of machines and of the design interaction that happens between them, [as well as] how those things can inform each other,” said symbolic systems associate director Todd Davies ’84 M.S. ’85 Ph.D. ’95 in an interview with The Daily.  

A symbolic system is essentially a system built with symbols such as natural language, programming languages, mathematics, or formal logic. An interesting thing is that symbolic systems are understandable by both humans and by computers.

You can get a more detailed understanding of symbolic systems from the Stanford Bulletin which describes the course.  Cognitive science is somewhat similar to symbolic systems. Computational linguistics is also somewhat similar.

Why is this important?

In his book Saving Capitalism, Robert Reich describes (page 204-206) three categories that all modern work/jobs fit into: 

• Routine production services which entails repetitive tasks,
• In-person services where you physically have to be there because human touch was essential to the tasks, 
• Symbolic-analytic services which include problem solving, problem identification, and strategic thinking that go into the manipulation of symbols (data, words, oral and visual representations). 

In describing the third category, symbolic-analytic services, Mr. Reich elaborates:

“In essence this work is to rearrange abstract symbols using a variety of analytic and creative tools - mathematical algorithms, legal arguments, financial gimmicks, scientific principles, powerful words and phrases, visual patterns, psychological insights, and other techniques for solving conceptual puzzles. Such manipulations improve efficiency-accomplishing tasks more accurately and quickly-or they better entertain, amuse, inform, or fascinate the human mind.”

Think Computational Law and Computational Audit.  Many tasks in accounting, reporting, auditing, and analysis are related to symbolic-analytic services and rearranging abstract symbols. As I pointed out a while back, the "Learn to code" is a hysteria and is misguided.

The Essence of Accounting has a lot of information that will help you get your head around the accounting symbolic system.  The Logical Theory Describing Financial Report will help you learn about reports.  Understanding Digital helps tie accounting, reporting, auditing, and analysis together.

More advanced information is provided by Processing Logical Systems. One more thing worth checking out if you are interested in all this is Introduction to the Fact Ledger.

Or, if you want the "full meal deal" and want to work through all my best information methodically, deliberately, and rigorously; please see: Mastering XBRL-based Digital Financial Reporting.

In a prior blog post I mentioned Computational Law.  My first question after seeing that was, "Is there such thing as a computational audit?"  Well, turns out that there is.

Per this paper, Providing Continuous Assurance, since about the 1990s a family of computational audit approaches have been developed.

Also, seems to me that AI assisted audits have a heavy computational aspect to them. Seems like the AICPA Dynamic Audit Solution Initiative is a step toward computational audit.

The Essence of Accounting points out how computational accounting is.

This web site called Computational Auditing appears to already do royalty payment auditing among other things.

An excellent paper by Michael Genesereth of Stanford University's Center for Legal Informatics, Computational Law: The Cop in the Backseat, provides insight into what is about to happen to financial accounting, reporting, and auditing.

The paper provides this definition of computational law and example of a rudimentary computational law system:

Computational Law is that branch of legal informatics concerned with the codification of regulations in precise, computable form. From a pragmatic perspective, Computational Law is important as the basis for computer systems capable of doing useful legal calculations, such as compliance checking, legal planning, regulatory analysis, and so forth.

Intuit's Turbotax is a simple example of a rudimentary Computational Law system. Millions use it each year to prepare their tax forms. Based on values supplied by its user, it automatically computes the user's tax obligations and fills in the appropriate tax forms. If asked, it can supply explanations for its results in the form of references to the relevant portions of the tax code.

Given that statutory and regulatory financial accounting and auditing rules are effectively laws, it does not take much imagination to see the possibilities here.  This helps one understand why things like the Logical Theory Describing Financial Report are actually very important.

A detailed but technical example of computational law is the British Nationality Act as a logic program.

Seems like these guys have the same idea that I do related to "blocks": Blawx | GitHub | Demo

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Potential for bias and discrimination

LegalBlocks (Blockchain)

Why Computational Law?

When trying to better understand the connection between ontologies, models, rules, and facts; I ran across this resource.  It is a treasure trove of information.  Specifically (this is just a sampling): 

• Ontologies & Models
• Caminao Ontological Kernel
• The Book of Fallacies
• FOCUS: Data vs Information
• Subtyping Semantics (set-theoretically defined subtyping relation)
• Specialization vs Generalization
• Ontologies and Enterprise Architecture
• Domains
• Structures
• Rules
• Model Validity
• Risks
• Quality

Here is a useful tidbit:

"Given the higher perspective it may be tempting to associate ontologies with meta-models, but that could be misguided because meta-models are still models whose purpose is to describe and process other models, as compared to ontologies which are only concerned with meaning and semantic consistency."

There is a lot here to be digested!