Digital Financial Reporting

Digital financial reporting can be explained by the following graphics. Note that you can click on any of the images to get a larger image.  Also note, that this video also covers this information.

First, note that I am seeing the following trends in financial reporting that are evolving in their individual "silos" but will eventually integrate with one another: 

• Machine-based digital reporting
• Human-machine collaboration
• Integrated reporting
• Push reporting
• Continuous reporting
• Continuous auditing
• Triple-entry accounting
• Algorithmic analysis/regulation 

This graphic provided by the Data Coalition shows what they refer to as "Smart Regulation".  In the graphic you see that they refer to "standard data models" (i.e. machine-readable rules) and the "engines of automation" (i.e. rules engines and artificial intelligence):

It is not going to be the case that enterprises keep their same old systems and "bolt on" new stuff, doing more work, just so that they can make regulation more efficient (but remain inefficient internally). And it will not be the case that each enterprise creates it's own model.  Think of the notion of a global standard common enterprise model for any organization to improve their internal processes and tasks.

The inner workings or processes and tasks of a financial reporting system of a typical enterprise is explained by this graphic inspired a Blackline graphic related to continuous accounting which I built on:

Those processes and tasks are the candidates for automation. Will everything be completely automated? That is very doubtful.  Will nothing be automated?  That is likely doubtful also.  The answer will be somewhere in between today's grueling, barbaric manual processes and the notion of "lights out finance" where the entire process is totally automated.

So, how will that automation be achieved?

This graphic, inspired by a similar graphic created by Dr. Leo Obrst and Mills Davis on slide 60 of this SlideShare presentation, shows the relationship between knowledge representation capability of different technical approaches and of reasoning capacity that can be achieved within a software application:

Note that there are many alternative approaches, each has its set of capabilities, many different ways will work, and a logical theory is one of the better approaches because it is highly expressive.

This graphic shows how global standard XBRL can be used to link different aspects of accounting, reporting, auditing, and analysis together so that information can be exchanged between tasks and processes:

So that is the big picture in pictures.  This example small system helps you understand the details of how all this actually works.

I provide a more complete explain of the moving parts in the document Special Theory of Machine-based Automated Communication of Semantic Information of Financial Statements. This video play list, Understanding the Financial Report Logical System, provides an overview of that document.

To understand that document, it is best if you read Artificial Intelligence and Knowledge Engineering in a Nutshell go get important background information.

Why do any of this?

As pointed out in the Accountant's Handbook on page 3 (D. R. Carmichael, O. Ray Whittington, Lynford Graham); financial reporting benefits society and improving financial reporting increases that benefit:

And so, the business case for XBRL-based digital financial reporting is better functioning capital markets and an improvement in the well being of society:

What do you think?

Here is a visualization of the logical conceptualization of a financial report. The Financial Report Semantics and Dynamics Theory has the same logical conceptualization in narrative form. The OMG Standard Business Report Model (SBRM) standardizes and formalizes that model.  XBRL is a global standard technical syntax for implementing that formal logical conceptualization.

And so, what will this look like for accounting professionals, financial analysts, and other business professionals?  Well, here is one version of what it will look like:

But it could look like many different things because software can reconfigure the pieces to suit your needs.  For example, it might look like this Excel-based automated comparison of 33 Microsoft financial statements (click the image to run the Microsoft comparison or any comparison of 22 other public companies; alternatively, try one of these Excel extraction tools that compare information for thousands of public companies):

The possibilities are endless, digital financial reporting is just getting started!  If you want more details, Intelligent XBRL-based Digital Financial Reporting likely has what you are looking for.

Financial reporting will be different in the fourth industrial revolution. There seems to be a number of trends that are interacting with one another in the realm of financial reporting.  This is a summary of what I see:

• Machine-based digital reporting: Machine-based digital reporting will likely prevail over the historically prevalent exchange of paper or e-paper by humans. (See this)
• Human-machine collaboration: Machines working along side humans to perform work much like a calculator helps humans do math, as contrast to the current overly manual processes. (Think good-old fashioned rules-based expert systems like TurboTax.)
• Integrated reporting: Sometimes called "triple-bottom line" or "sustainability reporting" or "ESG" as contrast to simply financial reporting. (Check out Larry Fink's letter to CEOs related to climate change.)
• Push-reporting: Push-based reporting as contrast to pull-based reporting. (Example)
• Continuous reporting: Continuous reporting as contrast to the historical "batch" oriented mode of reporting. (See the Finance Factory)
• Continuous auditing: Continuous auditing and AI assisted auditing using things like the dynamic audit solution as contrast to older audit approaches.  (See Imagineering Audit 4.0)
• Triple-entry accounting: Triple-entry accounting leveraging digital distributed ledgers as contrast to double-entry accounting which is internally focused.
• Algorithmic analysis/regulation: Algorithmic regulation or "smart regulation" as contrast to older approaches which tended to be more manual.

Center to all of this is the financial report logical model, enabling a new modern approach to financial reporting.  Software interacts with that model to put things into a report, make sure the report is right, extract information from the report for analysis, etc. Machine-readable metadata glues everything together logically, enabling very significant automation.

Imagine a financial report (or business report for that matter) that combined all of these three characteristics/capabilities: 

1. Nice formatting providing "eye candy" to humans.
2. Inline XBRL providing human-readable details within that nicely formated document, plus fundamental machine-readability.
3. Human readable details; current Inline XBRL viewers are OK, but there is a lot of room for improvement.  For example, seeing that all the mathematical computarions ticked and tied. A standard view of reported facts.

This video that explains how to turn gaseous "facts" into liquid "fact tables" and ultimately into human-readable renderings.

How is your digital maturity? Do you understand that this is more about people and organizational dynamics as contrast to simply technology?  How well do you understand artificial intelligence?  By one account, 81% of business leaders don't understand AI.  Do you have the "technology skills" people like the AICPA say you need?  Personally, I don't believe that you need technology skills; what you need is Computer Empathy.

Did I miss something?  Let me know.  Get the big picture here.

Apparently there is a Digital Reporting Convention in Vienna, Austria!  Seems to be put on by Nexxar.  Seems they have a technique called push reporting.  They are talking about the changing role of the corporate report:

Changed role of corporate reports

The role of the corporate report as an information medium has changed fundamentally in the last two decades. For a long time, the corporate report was not only regarded as the central source of information, but also as the only source of information regarding the economic development of a company. In the digital age, the report has lost this monopoly position. On the Internet, it competes with numerous other – often algorithm-based – information providers. Already on the day of publication, central contents such as the Balance Sheet or Statement of Income of a listed company are available on numerous large and small online portals

on the web (e.g. Google Finance).

 

The accounting equation is a tiny logical system, but using that as a tool you can create excellent sophisticated examples that people can get their heads around to understand the fundamentals of how XBRL works.  I summarized information for about 10 different fundamental impediments to creating a properly functioning financial report logical system in a new version of the accounting equation logical system.  This document, Impediments to Creating Properly Functioning XBRL-based Reports (AE), will help you master XBRL-based digital financial reporting.  Or, you can get all the information from this link.

Similarly, the same thing was done using the FASB's SFAC 6 Elements of Financial Statements.  Here is the document Impediments to Creating Properly Functioning XBRL-based Reports (SFAC6).  Here is a link to all the information about SFAC 6.

Those two examples provide the basis for understanding my Special Theory of Machine-based Automated Communication of Semantic Information of Financial Statements.

This article by Narayanan Vaidyanathan, Explainable AI: Putting the User at the Core, published by the Association of Chartered Certified Accountants (ACCA), is spot-on when AI is used in accounting, reporting, auditing, and analysis.  Don't miss the full report which you can get here.

What I have created is 100% explainable.  That is what I mean by "justification and explanation mechanism" (see here).  The user of the software should be able to follow the chain of reasoning used by any software application helping them. SBRM is likewise 100% explainable.

Here is part of the executive summary of this article:

Explainable artificial intelligence (XAI) emphasises the role of the algorithm not just in providing an output, but also in sharing with the user the supporting information on how the system reached a particular conclusion. XAI approaches aim to shine a light on the algorithm’s inner workings and/or to reveal some insight into the factors that influenced its output. Furthermore, the idea is for this information to be available in a user-readable way, rather than being hidden within code.

Historically, the focus of research within AI has been on developing and iteratively improving complex algorithms, with the aim of improving accuracy. Implicitly, therefore, the attention has been on refining the quality of the answer, rather than explaining the answer. But as AI is maturing, the latter is becoming increasingly important for enterprise adoption. This is both for decision making within a business, and post-fact audit of decisions made. Auditable algorithms are essentially ones that are explainable. 

The complexity, speed and volume of AI decision-making obscure what is going on in the background, the so-called ‘black box’ effect, which makes the model difficult to interrogate. Explainability, or any deficit thereof, affects the ability of professional accountants to display scepticism. In a recent survey of members of ACCA and IMA (Institute of Management Accountants), those agreeing with this view, 54%, were more than twice the number who disagreed. It is an area that is relevant to being able to trust the technology, and to being confident that it is being used ethically. XAI can help in this scenario with techniques to improve explainability. It may be helpful to think of it as a design principle as much as a set of tools. This is AI designed to augment the human ability to understand and interrogate the results returned by the model.

If you are unclear about AI, I would recommend this document.

The Data Coalition also talks about explainable AI.