Digital Financial Reporting

In a Financial Review article, Accounting systems need to come out of the 19th century, Petter Wells points out that accounting systems are stuck in the 19th century.  He points out how XBRL can be leveraged to improve audits:

100 per cent check

Professor Wells said adopting software such as XBRL would enable a computer-generated check of 100 per cent of audits, so the regulator could then look into just those flagged as problematic and not rely on its own classification of risk.

In my view, Evan Sparks is right in his Forbes article What Andreessen Horowitz Got Wrong about AI; artificial intelligence will be a low priced commodity.

Indeed, better software infrastructure for AI and Deep Learning development, coupled with commodity pricing on basic GPU and AI cloud services will do for the AI industry what Java, AWS, Intel, Hadoop, and hundreds of other companies and technologies did for SaaS. 

Standards, standards, standards.

Way back in 2000 with the help of the Urban Institute, MIP Software, and Practitioners Publishing Company (now part of Thompson Reuters) we created an XBRL taxonomy for not-for-profit reporting.  We used XBRL 1.0.  Needless to say, it was not a very good XBRL taxonomy and I am pretty sure no one used it.

Fast forward to 2020.  A lot more is known about XBRL taxonomies, the Treasury Department could very well be requiring certain not-for-profits to submit their financial reports in support of grants using the XBRL format, and "digital" is on the minds of more people.

The Grant Reporting Efficiency and Agreements Transparency Act of 2019 (GREAT Act) will undoubtedly have an impact on not-for-profit financial reporting.

And so, I figured that I would update that older XBRL taxonomy for not-for-profit financial reporting using everything that I have learned in the past 20 years.

Here are several example reports that might be created using an XBRL taxonomy for not-for-profit financial reporting: 

• Bill and Melinda Gates Foundation
• United Way
• American Red Cross

Essentially, many but not all not-for-profit entities report using US GAAP with some specific adjustments. Not-for-profit entities are covered in topic 958 of the Accounting Standards Codification (ASC). They are subject to the accounting equation like all other financial reporting schemes. They are subject to SFAC 6 Elements of Financial Statements like all other US GAAP financial reporting.

So, why would not-for-profits not use the existing US GAAP XBRL Taxonomy?  There are two reasons.  First, that existing XBRL taxonomy is primarily for financial reporting by public companies to the SEC.  So, it has a lot of "stuff" that is simply not relevant for not-for-profit entities.  Besides, that taxonomy does not have the specific features needed by not-for-profit entities.  Secondly, the existing US GAAP XBRL Taxonomy is not designed appropriately.  Essentially, that XBRL taxonomy tends to be more of a human-readable "pick list" than a machine-readable tool.  Further, all of the quality issues that are being experienced by public companies submitting information to the SEC provides emperical evidence that it does not work effectively.

Both the existing US GAAP and IFRS XBRL taxonomies suffer from issues caused by underlying design choices.  If you want a thorough understanding of these issues I would encourage you to watch this video play list, read this document, and then experiment with these working prototypes.

Inputs that will be used to create this XBRL taxonomy are the three example reports shown above for the Bill and Melinda Gates Foundation, United Way, and American Red Cross for starters.  That will expand.  Other inputs will include:

• ASC Topic 958
• The existing US GAAP XBRL Taxonomy
• The Unified Chart of Accounts (UCOA) created for not-for-profit entities
• PPC guidance
• Other resources like this operating reserve policy toolkit, not-for-profit GAAP guides, etc.
• The 2000 version of the not-for-profit XBRL taxonomy

I am going to try and get some other organizations involved including:

• National Center for Charitable Statistics (part of the Urban Institute)
• National Council of Nonprofits
• Center for Nonprofit Excellence
• Charity Navigator
• National Council of Nonprofits
• GWSCPA Educational Foundation
• GuideStar
• Organizations that support not-for-profits such as Aplos and YPTC and JJCO
• Software vendors

The objectives of this project are the following:

1. Create a high-quality state-of-the-art XBRL-based taxonomy for not-for-profit financial reporting that provably works based on rigorous testing.
2. Leverage the best ideas provided by XBRL-based financial reports created by public companies and submitted to the SEC, but avoid existing, known problem areas.
3. Get software vendors to understand and leverage that XBRL taxonomy and the benefits it provides in financial report creation software that is easy for business professionals to make use of.
4. Enable the possibility of creating an expert system to help users creating not-for-profit financial reports driven by the XBRL taxonomy's metadata.
5. Improving the state-of-the-art and hold this XBRL taxonomy out as a best practice example of how financial reporting taxonomies can, and likely should, be created.
6. Help professional accountants better understand XBRL-based financial reporting.

Anyone that wants to help or learn is invited to participate.  Send me a message on LinkedIn and I will get you pointed in the right direction.

There are some pieces of the XBRL taxonomy (NOTE!!!! this is a work in progress, many pieces still do not work yet)

• Home page
• Topics
• Disclosures
• Networks
• Terms
• XBRL taxonomy entry point
• Viewer
• Human readable information (reference implementation)

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UK Charities XBRL Taxonomy (see bullet point 11)

Online version of UK Charities XBRL Taxonomy

UK Charities XBRL Taxonomy Model Structure

Not-for-profit accounting

This blog post provides information related to the analysis of the Microsoft 2017 10-K.  This has to do with controlling the process of creating the report and analyzing information the report contains.  This is information about the report: 

• Video walking you through the analysis
• SEC filing page
• XBRL instance
• XBRL Cloud Evidence Package
• Pacioli Power Tool Analysis Package
• Rendering of Report (Autogenerated)
• Disclosure Mechanics Validation Results, XBRL Cloud (about 68 disclosures, about 50% of report)
• Disclosure Mechanics Validation Results, Pesseract (about 124 disclosures, 94.8% of report)
• Disclosure Mechanics Rules (Human Readable | Machine Readable)
• Blocks
• Flat list of statements from the report
• Extracting report information
• Knowledge Graph of Information Models

Most current prototype:

• XBRL instance with rules attached directly to the report
• Pacioli validation results for above report
• XBRL Cloud validation results for above report
• Visual image of what I mean by "pieces" (note how verification linkbases and schemas are tied to the report)
• Pacioli list of disclosures
• Pesseract list of disclosures
• More information

This XBRL-based report contains 194 sets of facts which I used to call "blocks" and now I think I call "fact sets".  For this report, I can identify 94.8% of those 194 sets of facts. This is important because (a) the rules can be used to make sure the report is created correctly and (b) the rules can be used to effectively and reliably extract information from the report.

I am comparing the Microsoft 10-K to the 10-Ks of Amazon, Apple, Facebook, Google, and Salesforce.  The primary thing I am noticing is the propencity of accountants to focus on the presentation of information which is arbitrary/subjective and the representation of information which tends to be objective.

Also, I have completely recast the Microsoft 10-K in order to be able to control some things and perform some additional testing.  You might find that helpful.

Algorithms will be changing the world of accounting, reporting, auditing, and analysis over the coming years. An informal definition of algorithm is a "a set of rules that precisely defines a sequence of operations".

In his book (page 257) Principles, Ray Dalio explains algorithms, "Algorithms work just like words in describing what you would like to have done, but they are written in language that the computer can understand."  He goes into this in more detail, explaining how humans and machines will work toghether in this video.

For more information about algorithms, watch this video.  That video is made for business professionals.

For more depth, watch this Intro to Algorithms: Crash Course in Computer Science #13.