Digital Financial Reporting

Business rules can be grouped into two broad categories: data quality logic related and business logic related.  Of course, your first question might be "What the heck is a business rule?"  A business rule, as defined by the Business Rules Group in their Business Rules Manifesto is:

Business rule: A formal and implementable expression of some user requirement.

In their Decision Model, Knowledge Partners International points out the important difference between data quality logic and business logic:

• Data quality logic: is the logic used against data elements to determine if they meet various data quality dimensions such as completeness, reasonableness, etc.
• Business logic: is the logic that uses data elements as conditions leading to business-oriented (not data-validation-oriented) conclusions such as compliance, eligibility, etc.

I have turned my analysis of SEC XBRL financial filings from the primary financial statements to the disclosures. I picked a somewhat common disclosure to take a look at: long-term debt. As an accountant I understand that not every reporting entity has long-term debt.  But if a reporting entity does have long-term debt, then specific disclosures are required.  Further, because of the way the SEC EFM says XBRL-based financial reports need to be created, I would expect them to look a certain way.

This is a summary of what I found from my set of 7160 financial filings (all 10-Ks): 

• 1,571 filings, which is about 22%, contained something that indicated that they had long-term debt.  Typically this would be the line item "Long-term debt" reported on their balance sheet.
• Of that total, 469 reporting entities provided BOTH a detectable long-term debt maturities disclosure and a detectible break down of their debt instruments.  That is about 30% of reporting entities.  I would expect 100% of reporting entities which have long-term debt on their balance sheet to provide both of these disclosures.  I would assume that these disclosures exist, I just need to make my detection algorithms more sophisticated.
• Of the 1,571 filings, there were 1,553 filings, which is 88% which provided a "Debt Instruments [Table]" (using the report element us-gaap:DebtInstrumentTable).  On that [Table], 1,170 or 75% had a "Long-term Debt Type [Axis]", 1,103 or 71% used a "Debt Instrument [Axis]" and 772 or 50% used both of these [Axis].
• 996 or 64% of the reporting entities who had long-term debt provided the [Text Block] provided in the US GAAP XBRL Taxonomy "Schedule Of Maturities Of Long-Term Debt [Table Text Block].  The rest did not.
• Of the 1,553 which provided that "Debt Instruments [Table]"; only 779 about 50% provided the "Schedule Of Debt Instruments [Text Block]".

Those are only some of the things I observed relating to long-term debt in the SEC XBRL financial filings which I am analyzing.  I don't know if these are data quality logic anomalies or business logic anomalies.  They seem like data quality logic anomalies to me.

I mean, if someone provides a "Debt Instruments [Table]" which is the details of debt instruments it would be logical to expect that the "Schedule Of Debt Instruments [Text Block]" should be located also because the EFM requires both levels of information.  Particularly since this is true for 50% of SEC XBRL financial filers.

What do you think?

I would not go as far as saying that it is a RoboCop, but I do have a rudimentary accounting analysis software agent up and running against SEC XBRL financial filings.  I am not trying to fight fraud or discover accounting shenanigans; I am simply trying to help accountants understand financial reporting and digital financial reporting best practices. I am also trying to understand how to construct quality XBRL taxonomies and what business rules are necessary to make a system work effectively.

Below is a summary of my results of my first query.  The question I was trying to answer was to see how many SEC XBRL financial filings provided a future minimum lease payments under capital leases disclosure, see what balance sheet line items they provided, and see whether I could find both a text block level disclosure and a detailed disclosure.  Here is a summary of my results:

In addition to the software agent which looks through the details of the filings, I have some other software which helps me analyze the filings.  I put together this document of a summary of that analysis for this specific disclosure. I can do this sort of analysis for any financial report disclosure.

Again, while what I have created is fairly rudimentary, I think it does help people see some of the types of things one can do with a digital financial report using automated processes which could only be done manually using typical reports which are only readable by humans.