BLOG:  Digital Financial Reporting

This is a blog for information relating to digital financial reporting.  This is my brain storming platform.  This is where I think out loud (i.e. publicly) about digital financial reporting. It is for innovators and early adopters who are ushering in a new era of digital financial reporting.

Much of the information contained in this blog is synthasized, summarized, condensed, better organized and articulated in my book XBRL for Dummies and in the chapters of Intelligent XBRL-based Digital Financial Reporting. If you have any questions, feel free to contact me.

Repository Prototype

This is a prototype of an information repository.  I want to turn this into a complete knowledgebase.  More to come; stay tuned!

Posted on Tuesday, February 13, 2018 at 07:33AM by Registered CommenterCharlie | CommentsPost a Comment | EmailEmail | PrintPrint

Updated Excel-based Validation Tool With Line of Reasoning

I mentioned some Excel-based extraction and validation tools that I created.

Well, I created an improved version.  There are two significant improvements to this fundamental accounting concepts validation tool:

  1. Validate one file at a time.  Rather than validating many different files and then comparing them like the other tools, this one validates ONE filing at a time to let you focus on that filing.  Again, you can use a URL from the SEC EDGAR repository or a local file.
  2. I added what I call a "line of reasoning" output to help you understand what the tool is doing.

Let me know what you think.  I believe the line of reasoning turned out nice.  I know that there is a lot of room for improvement such as formatting the numbers, providing additional information, etc.  You can add that functionality yourself.

Posted on Friday, February 9, 2018 at 03:50PM by Registered CommenterCharlie in , | Comments1 Comment | EmailEmail | PrintPrint

Disclosure Best Practices (Prototype)

I have perfected what I call a Disclosure Best Practices resource. I am envisioning this as a resource for intermediate accounting students to begin with. Then it could be expanded and made useful to professional accountants that external create financial reports.

This early prototype which took the form of a document helps you understand where this Disclosure Best Practice resource is going. The conceptual model of a financial report also helps you understand this resource.

There are two important things that might not be apparent by just looking at this resource.  First, the information you see is 100% machine readable as well as being readable by humans.  Second, the organization of the information into the form you see is 100% automated. That organization is achieved using metadata from the conceptual model.

If you don't understand this Disclosures Best Practices resource or why it is important, you should consider reading the document Closing the Skills Gap.

The foundation for this Disclosures Best Practices is the Reporting Checklist and Disclosure Mechanics machine-readable business rules.  Validation is run to make sure you only get examples that are of high quality.  So for example, here is the validation of the Microsoft 10-K.  Imagine that same information for (a) all public companies that report to the SEC and (b) reading the information using automated machine-based processes rather than reading the information in a web browser.

Stay tuned for more information.  If you want to keep on top of all this, participate in the campaign to improve disclosure quality.  By March 31, 2018 the 65 disclosures of the campaign and the disclosures in the Disclosure Best Practices resource should be in sync.  Then; I will start adding more disclosures to expand the set available.

Be sure you fiddle with the prototype.  There is more there than you might recognize by just glancing at it.  This is the primary entry point into the resource.  There are about 50 different disclosures.  You can see information about each of the some 50 disclosure. You can get information about economic entities that create the reports.  Information is linked to US GAAP XBRL Taxonomy information and the Accounting Standards Codification.  Topics which will enhance filtering and searching for disclosures will be incorporated. More metadata exists to filter reporting entities by reporting style, by accounting activity, by sector, by size of the company, and other such information is incorporated, but is not apparent yet.

One next step for me is to create this same resource for the XASB reporting scheme. The reason for that is to show that the conceptual model is not specific to US GAAP or even IFRS; it is general. Ultimately, it will work for all of these profiles.

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Customizable Tool for Analyzing Text Block Disclosures

I took that disclosure analysis tool and further modified it.  Not, it is customizable and you can change the concepts being looked for by simply adding a text block concept to a row in a spreadsheet.  Here are three prototypes:

Here is a link to 65 disclosures that I am analyzing during my campaign to improve disclosure quality.  I will create some documentation and provide that.

For now, if you go into the column with the stuff that looks like HTML (Column "L") and you double click the cell, a form opens up and the HTML for the Level 3 Disclosure Text Block so you can read the disclosure.  The point of the Excel application is to example how public companies are using each text block to see if they are using them consistently.

Harvard Business Review: The State of Machine Intelligence

In their Harvard Business Review article, The Competitive Landscape for Machine Intelligence, Shivon Zilis and James Cham summarize the state of machine intelligence (which they say is a more neutral term for artificial intelligence).

The article provides a PDF titled The State of Machine Intelligence, 2016 which summarizes somewhat of a machine intelligence "stack".

Here are two points made by the article:

Machine intelligence is not just about better software; it requires entirely new processes and a different mindset. Machine intelligence is a new discipline for managers to learn, one that demands a new class of software talent and a new organizational structure.

Every employee can use machine intelligence to become more productive with tools that exist today. Companies have at their disposal, for the first time, the full set of building blocks to begin embedding machine intelligence in their businesses.

Business professionals, just like professional accountants, need to gain a few new skills to understand that this change is real and it will be significant.

Posted on Wednesday, January 31, 2018 at 07:17AM by Registered CommenterCharlie in | CommentsPost a Comment | EmailEmail | PrintPrint
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