BLOG: Digital Financial Reporting
This is a blog for information relating to 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 summarized, condensed, better organized and articulated in my book XBRL for Dummies and in the three documents on this digital financial reporting page.
Every craftsman values good tools. While a trained craftsman can use any tool and likely still do a better job than an unskilled or under-skilled person performing the same task; craftsmen still value good tools.
Also, a good tool can turn an under-skilled person into a more highly-skilled person if the tool reduces ignorance (i.e. provides increased knowledge).
What can be a problem is if a lesser-skilled person is unaware of things they should be concerned with but the tools they use don't help them to overcome this deficiency in skill or experience.
Accounting professionals working with digital financial reports such as XBRL-based financial filings to the SEC suffer from a lack of quality tools. The good thing is, that is changing.
I figure that I am 100 times more productive today than I was only one year ago. This is no exaggeration. Click on the image below and look at the interface that I created for reviewing public company XBRL-based financial reports:
The inconsistency is not visible at all in the actual filing, everything foots correctly and everything else looks just fine. But if you notice the review tool on the left that shows the YELLOW cell, you immediately understand that something is up. If you look closely, you notice that this filer used the concept "us-gaap:AssetsNoncurrent" incorrectly. That concept includes property, plant, and equipment per the US GAAP XBRL taxonomy but if you look at the assets section of the financial report, you see that PPE is not included in the total used to express the fact they are reporting.
So, there are two errors here really. The first error is that the concept the reporting entity is looking for does not exist in the US GAAP XBRL Taxonomy. It needs to be added, that is the first issue. The second is that rather than creating an extension concept, the filer picked some concept that to them seemed close. This conveys the wrong information.
Those are the sorts of issues the fundamental accounting concepts and relations makes very easy to see. Two very, very positive things are happening. First, tools are improving. Second, lots of accountants have experience with using XBRL now after five plus years of creating digital financial reports.
But unfortunately, these sorts of nuances can be hard to recognize with less than optimal tools. So accountants, pester your software vendors for better functionallity.
Here is another screen shot, this time with a digital financial report which is consistent with expectation:
This tool I built myself using Microsoft Access fiddling around with pieces of functionality provided by XBRL Cloud. The tool provides a side-by-side comparison of a financial report with a validation report used to check the consistenty of the financial report. It make it easy to figure out why there is some sort of inconsistency. If you want to know more about this, send me an email and I can provide additional information.
OK, so it only has two entries currently, but that is not the point. The point is, Digital isn't software, it is a mindset. If you understand how financial reporting is going to work in the future then you understand that being able to represent knowledge in machine-readable form is a good thing.
I created the commentary linkbase using Microsoft Access as a working prototype. You can download the Microsoft Access database here. Don't understand how to program? Well, maybe you want to learn. Don't understand XBRL. Well, this is a way to learn about XBRL.
What I am going to use the commentary linkbase for is to help public companies get the fundamental accounting concept relations correct. What will you use it for?
The quality of public company XBRL-based digital financial reports increased yet again. This month 3 generators (software vendors, filing agents) reached the point where 80% or more of their filings where consistent with a set of fundamental accounting concept relations.
A few data points are worth pointing out. First, the sum of errors in all filings related to these fundamental accounting concept relations is about to drop below 3,000. Right now, the sum of all errors is 3,001. Contrast that to the 12,259 total inconsistencies for the 2013 10-Ks and and 4,463 total inconsistencies for the 2014 10-Ks.
Second, if you look at the graphic on the very bottom you can see that consistency with each test is now above 95% for every test except for one which is at 94.98%. Next month, every test will likely be above 95%.
Here is a comparison across time which shows total reports as the blue bar and total number of 100% consistent public company XBRL-based financial reports which is the red line:
Here are the results summarized by generator:
Here are the results summarized for the individual tests:
You can find the human-readable and machine-readable versions of of the consistency checks used to test these fundamental accounting concept relations here.
I am changing the way I evaluate consistency with these fundamental accounting concept relations tests slightly going forward. In the past, I have combined the consistency checks with the machine-readability checks. My rational is as follows. In the past I have had the mantra,
"Prudence dictates that using financial information from a digital financial report not be a guessing game."
There are two parts related to achieving machine-readability of the financial information reported by public companies. The first is the consistency of the fundamental accounting concept relations which are expected. The second is providing adequate information for a machine to be able to decipher the information.
The first, consistency with expected relations, is a US GAAP accounting issue. Basically, using the wrong concepts or mathematical errors are accounting consistency issues.
The second, the ability of a machine to be able to correctly decipher information is not a US GAAP accounting issue. There are plenty of perfectly legal US GAAP ways to report information that my rather unsophisticated algorithms for reading reported information cannot read. If the software algorithms were more sophisticated, I could read the information. Basically, public companies don't have to report so that simple algorithms can read their reported information. In my personal opinion it would be better if algorithms were easy to create, that minimizes the guessing game and increases the probability of software vendors creating software get algorithms right.
And so, I have found a way to distinguish these two different things. I will explain this next month when I roll this new approach out. Also, because the number of inconsistencies is getting so low, I am able to summarize every inconsistency category. This will help understand inconsistencies and tune the US GAAP XBRL Taxonomy. Stay tuned.
XBRL-US and five members including Merrill, RR Donnelley, RDG Filings, Vintage, and Workiva have organized to take on the quality of public company XBRL-based financial filings to the SEC. See this blog post by XBRL-US.
Microsoft Word and Excel are used to create an estimated 85% of all external general purpose financial reports. But what do Word and Excel understand about financial reports? The answer is that Word and Excel know nothing about financial reports. What if the software application used to create financial reports did understand financial reports?
Understanding digital financial reporting and its benefits
Digital financial reporting is financial reporting using structured, machine-readable form rather than traditional approaches to financial reporting which are paper-based or electronic versions of paper reports such as HTML, PDF, or a document from a word processor which is only readable by humans. A digital financial report is readable by both humans and by machine-based processes. Because digital financial reports are machine-readable the machine can be more intelligent and helpful in understanding where a professional accountant is working in a financial report (i.e. context-aware) and therefore make the software more adaptive, more dynamic, able to provide an advisory role to professional accountants, be more proactive, and provide other knowledgeable guidance related to the creation and review of a financial report. Unlike word processors which understand nothing about a financial report, digital financial report software has an intimate understanding of a financial report.
Digital financial reporting includes the creation of general purpose financial reports under IFRS, US GAAP, governmental accounting standards, or other reporting schemes. Other reporting schemes may also use this global standard approach to creating a machine-readable digital financial report. The focus here is not specifically what goes into a financial report per one specific reporting scheme; but rather it is about the digital financial report itself. Digital financial reports can be used by any reporting scheme which might choose to express some financial or non-financial information digitally.
Digital financial reporting can be understood by contrasting that process to the process of creating blueprints using Computer-aided Design/Computer-aided Manufacturing (CAD/CAM) software . Just as CAD/CAM software is knowledgeable of blueprints; digital financial reporting software is knowledgeable of financial reports. CAD/CAM software understands what a door is, what a window is, what a wall is, and that a window goes into a wall. Similarly, digital financial reporting software understands what a balance sheet is, what an income statement is, what a disclosure is, that assets goes into the balance sheet and that assets equals liabilities and equity, per the accounting equation. CAD/CAM software is used to increase the productivity of the designer, improve the quality of design, improve communications through documentation, and to create a database for manufacturing. CAD/CAM output is often in the form of machine-readable information which can be printed; provide instructions for machining directly to a numerically controlled machine, or used in other ways for other manufacturing operations. Similarly, a digital financial report will travel through the entire supply chain which is connected via the Internet and information never needs to be rekeyed and different business systems will have the same understanding of the reported financial facts and the relations between the reported facts.
The machine's knowledge of a digital financial report is enabled by the structured nature of the information represented within the machine-readable financial report, metadata that explains business rules related to the creation of the financial report that the machine must follow, and meta-metadata which helps other participants of the financial reporting supply chain such as investors and analysts which make use of the reported financial information interact with these machine-readable artifacts to effectively and successfully exchange meaning between business systems and processes. Knowledge about the mechanics of a financial report and how to create a financial report is carefully expressed in machine-readable form by humans. This is not to say that all knowledge can or will be expressed; rather, only objective knowledge that makes a computer software program capable of helping its human operators can be expressed. Subjective knowledge, such as the judgment of a professional accountant, can never be expressed in terms that are understandable by a machine. Essentially, the machine mimics basic mechanical tasks related to the creation of a financial report. A machine gives you the sense that it is intelligent, it appears intelligent, because understands the context in which it is working.
The machine's understanding is enabled using software algorithms and machine-readable metadata and meta-metadata. Metadata and meta-metadata is stored and managed within formal and informal ontologies which describe the things that make up a financial report, important relations between those things, and other information related to the financial report knowledge domain in machine-readable form. Ontologies are created and managed by knowledge engineers who help business professionals create and manage this metadata. Ontologies both describe the business domain and serve to verify that digital financial reports created are consistent with that description. Machines can assist accounting professionals in the creation of financial reports to the extent that metadata about the financial report and how to create the financial report is articulated in machine-readable form. Another term for these relations is business rules. Understanding what business rules are and what they do is key in understanding the capabilities of machines to help professional accountants create and review financial reports. (For more information about business rules, see the Business Rules Manifesto.)
Current tools such as disclosure checklists which serve as memory joggers to humans are not machine-readable. In the context of digital financial reporting these current human readable checklists and memory joggers are made machine-readable and therefore many tasks which can be automated using machine-based processes will be performed by computers. As previously stated, processes which will be automated are the more mechanical aspects of creating a financial report, as opposed to the judgmental aspects which require the knowledge of a professional accountant to get correct. Digital financial reports will free both professional accountants who create these reports and financial analysts and regulators who use information from these reports from tasks such as re-keying information and making sure the objective aspects such as mathematical relations of a report are correct, allowing professional accountants to focus on judgmental and other subjective aspects which cannot be automated.
The benefit of digital financial reporting is enabling machines to take over mindless and mundane mechanical tasks that are involved in the creation of financial reports. Not all tasks, rather tasks that can be effectively achieved using machines. Enabling machines to take over tasks that had been performed by humans results in reduced costs involved in creating financial reports, reducing human errors because machines take over many mindless mundane mechanical tasks, increased quality and reduce the risk of noncompliance because machines take over these mindless mundane tasks, and less time to complete financial reports because of the assistance provided by automated machine-based processes. Automation can be achieved to the extent that machine-readable metadata and data is provided and that software algorithms can be written.
How do you get a computer to actively help you create a financial report? Semantics teaches a computer to understand what you mean. And so how do you construct such an application? This video, Process Execution Through Application Ontologies, provides some answers. The video outlines five steps:
Phase 1: Gather information
- Step 1 - Delivery: Determine the delivery (in our case, the delivery is a financial statement)
- Step 2 - Events: Determine the necessary events which should happen to make the delivery
- Step 3 - Event sequence: Determine the earliest sequence an event could occur relative to other events
- Step 4 - Process states: Determine the different states of the process
- Step 5 - Create a process diagram: Design/create a process diagram (perhaps using BPMN notation, here is a BPMN tutorial, here is a video that explains BPMN)
Phase 2: Classification of members
- Step 6 - Construct ontology: Create an ontology that represents everything gathered in Phase 1
Here is more information on this topic. Interesting...More to come.