Digital Financial Reporting

Automation is about removing friction, driving down costs, speeding processes up, and generally improving efficiency.  Automation is about delivering cheaper and better goods and services for less cost.

In previous iterations related to connecting accounting, reporting, auditing, and analysis; I learned about many of the issues related to creating those connections.  Examples of those issues can be distilled down to the following essence:

• Inappropriately set up chart of accounts.
• Inappropriate mapping between chart of accounts and financial report line items. (i.e. lead schedules)
• Information (metadata) used to correctly categorize report information missing from the accounting system therefore automation is impossible. (i.e. explicitly add information to system at the first opportunity where possible)
• Information that is unavailable to automated processes. (i.e. policies not in accounting system, qualitative disclosures not in accounting system)
• Errors. (i.e. lack of control processes, use of Lean Six Sigma philosophies and techniques)
• Complexity. (i.e. multi-currency, multi-gaap, multi-subsidiaries, multi-special ledgers, multiple accounting systems, etc.)

Sure, there are other issues but the above issues is a really good intitial list of what needs to be overcome.

So why is this important? If you watch the interview of Ray Dalio related to systematizing and computerizing decision making, or listen to IBM's CEO, or ask yourself why OMG and others created a digital twins consortium, or think about what it takes to create a mirror world; all this makes complete sense. (If you don't understand this now but want to, I would suggest starting here.)

For this most current iteration I am expanding an already working record to report prototype that I created.  That example was 95% complete before I learned about plain text accounting.  I incorporated plain text accounting journal entries supported by Ledger and hledger into that prototype, basically serializing the existing small set of journal entries from one general journal to the format supported by Ledger and then running the transactions through ledger to see what I got.

Next objective

My next objective is the following.  First, considering that everyone and their brother now says they have "stuff" that will help organizations make the digital transformation but none of them actually provide any real details; I wanted to create my own world-class, best practices-based, standards-based open source offering that actually works as proof that all this can, in fact, work and truly understand HOW to make it work effectively. (Here is KPMG's offering for comparison.)

Second, given that most accounting systems don't have the necessary metadata included I wanted to work with accounting systems that do allow you to add the necessary metadata; both Ledger and hledger fit the bill.

Third, given that most people cannot make the mental leap and believe that 14 journal entries from one journal can prove anything; I am going to expand the example to 187 journal entries from 7 different journals to make the example more believable to the hard to convince.

The objective is the following (i.e. connecting all these things for a machine in a form understandable and usable by business professionals; for more information see the Essence of Accounting):

1. Journal entries (i.e. input): Start with journal entries.  They are in my Microsoft Access database "accounting system" which I can PRINT to PDF, export to XBRL Global Ledger format, or export to plain text accounting format.
2. Process in accounting system: I want to be able to process the journal entries in the accounting system effectively.  This includes:
   1. Transactions register by account: hledger | Access
   2. Trial balance of accounts: hledger | Access | XBRL Cloud
   3. Roll forward summary analysis: hledger | XBRL Cloud
   4. Roll forward of accounts: hledger | Access
   5. Balance sheet: hledger | XBRL Cloud
   6. Income statement: hledger | XBRL Cloud
   7. Cash flow statement: hledger | XBRL Cloud
   8. Statement of changes in equity: hledger (nothing) | XBRL Cloud
3. External report (i.e. output): Generate an external financial report for the primary financial statements (for now); note that these examples are from a different prototype; the goal of this prototype is to generate all of this for the 187 transactions from the 7 different journals:
   1. External financial report: Human readable PDF | Raw XBRL | Inline XBRL (human and machine readable) | XBRL Cloud Proprietary Rendering
4. Audit external report: Be able to extract information from the external financial report such that reported information can be verified by an independent third-party accountant assisted by artificial intelligence tools.
   1. Artificial intelligence assisted audit: Mindbridge | AICPA | Dynamic Audit Solution Initiative | xAudit
   2. Example audit schedule which would be created for every audit lead schedule category.
5. Extract information for analysis: Extract information from report for automated analysis by a regulator, an investor, analyst, bank commercial loan department, internal benchmarking, variance analysis as part of external audit, etc.
   1. Analysis: Discounted cash flow model | XBRLogic | Excel Information Extraction Prototype | Information Repository prototype | Report Validation Dashboard
   2. Example of analysis model (Unlevered discounted cash flow model)

And so that is my objective; a working proof of concept that shows all of the above in a form that is understandable to both professional accountants and computer scientists.

To keep tabs on this project, watch this page.  Walk through this document.

How do I and other software vendors achieve all this?  If you want to understand, first invest in reading this document Artificial Intelligence and Knowledge Engineering Basics in a Nutshell which provides critically important background information. Next, read this Special Theory of Machine-based Automated Communication of Semantic Information of Financial Statements which explains how to effectively create the necessary capabilities. Finally, study the Logical Theory Describing Financial Report.

The market will decide what success looks like.

Finance transformation is a thing. PWC says that AI will create a huge market opportunity, $15.7 trillion, a 14% increase in GDP! Most people don't really understand AI correctly these days.  If you can figure this out and do it right as contrast to creating some cheap parlor tricks or a fragile house of cards; you can likely succeed.  As the Harvard Business Review points out, this is about talent, not technology.

"Silo-ization is a learned behavior that needs to be unlearned."

This is an excellent article that should be read by every business professional; it will help you understand the future: The Data-Centric Revolution: An Interview with Dave McComb.  Here is the summary:

Summary: Are today's economics of software projects and support inevitable? No. They are a product of the fact that the industry has collectively chosen the application-centric route to implementing new functionality. When every business problem calls for a new application and every new application comes with its own database, what you really get is runaway complexity. Many clients have thousands of applications. But it isn't inevitable. A few firms have shown the way out: data-centric development. In this ground-breaking interview, Dave McComb explains what being 'data-centric' is about and how it can be made to work.

It seems that everybody and their brother now thinks that improving accounting, reporting, auditing, and analysis tasks and processes is a good idea.  Some call this continuous accounting.  Others use different names.  Here are offerings or visions from: 

• KPMG: Performance on Demand for Finance (read this PDF)
• EY: The DNA of the CFO
• Deloitte: Preparing for the Future of Finance - Now
• PWC: Where should your finance transformation begin

So, I created my own offering which is a global standard proven relable best practices method for improving accounting, reporting, auditing, and analysis tasks and processes.

The cornerstone of my method is the logical conceptualization of a financial report. 

If you listened to the video of Ray Dalio being interviewed by Garry Kasparov where they talk about artificial intelligence, AI and Algorithmic Decistion Making, you heard Ray Dalio explaining how important it is to distinguish between “closed systems” as contrast to “open systems”.  I am in complete agreement.

Why is this important?  It is important to understand the difference between the reliability and predictability (i.e. tolerance for error) that you get from open versus closed systems and how much effort it takes to manage that control.  Systems need to be controlled.  Rework is expensive.

I have not found a discussion of "control" that specifically relates to computer systems.  I sometimes use the terms "finite" as contrast to "infinite" to describe what I am getting at.  Other times I used the notions of "open systems" as contrast to "closed systems".

I found three Wikipedia articles that discuss this notion: Closed system (control theory); Classical control theory, and Control theory.  I also found this article, An Introduction to Systems and Control Theory for Computer Scientists and Engineers, which points out:

“software engineers need to use control concepts to master the ever-increasing complexity of computing systems” 

Guaranteed performance is a good thing.  Stability is a good thing.  Being as high as possible on the knowledge representation spectrum is a good thing.

Admittedly, I do not understand how to describe this formally.  However, I do understand that being able to control a system such as an XBRL-based digital financial report is a business requirment.  It is not a "nice to have" feature; it is a required feature.

I try to explain this in this document. I can point out all that is necessary with this logical theory.  I can prove that I am getting this right with these working proof of concepts and this conformance suite. And all this can be supplemented using the techniques and philosophies of Lean Six Sigma.

All of this can be applied to implement continuous accounting, reporting, and auditing effectively.

Not clear on what needs to be controlled?  Watch these three videos: Impediments Part 1; Impediments Part 2; Impediments Part 3.

Also, I added this Process Control video to the Understanding Financial Report Logical System playlist.

A lot of people are talking about "continuous accounting" and "continuous reporting" and even "continuous auditing".  Some provide grand visions and buzz words to try and describe continuous accounting and reporting.  Some people even give you very broad brush strokes that help you understand what it means. Some provide high-level blueprints for implementing continuous accounting.

But think for a moment.  If you actually wanted to achieve continuous accounting, reporting, and auditing; exactly how would you do that? How exactly are you going to automate the tasks and processes involved?

First, what gets in the way of continuous accounting and reporting?  Here are some specific things that I see:

• Inappropriately set up chart of accounts.
• Inappropriate mapping between chart of accounts and financial report line items. (i.e. lead schedules)
• Information (metadata) used to correctly categorize report information missing from the accounting system therefore automation is impossible.
• Information that is unavailable to automated processes. (i.e. policies not in accounting system, qualitative disclosures not in accounting system)
• Errors.
• Complexity. (i.e. multi-currency, multi-gaap, multi-subsidiaries, multi-special ledgers, multiple accounting systems, etc.)

So all those things add up to the hurdles that need to be overcome to actually get continuous accounting and reporting to actually work.

Second, you actually have to physically implement something.  This video walks you through a basic implementation of continuous reporting.  So this fundamentally works.  Here are all the details of two implementations that have 100% of what is necessary for a basic working system where accounting tasks and reporting tasks are automated: Accounting process automation/record to report; Pixel perfect rendering.  (If you want the complete scope, you need all of this which is simply more volume of what is in the first two examples; but the two examples I point to really are complete.)

To make all of this work effectively, you must be able to control the system effectively.  And so, if you can overcome all of those hurdles and control the system effectively such that the system is reliable and predictable then you can use that system to perform useful work. Let machines do the grueling, gruesome, repetitive, mindless tasks.  Maybe not all tasks and processes will be automated, but certainly some will be automated.

All of this is about augmented intelligence.  Another term used is intelligence amplification.  In his book, Principles, Ray Dalio advises,

“By developing a partnership with your computer alter ego in which you teach each other and each do what you do best, you will be much more powerful then if you went about your decision making alone.”

Augmented intelligence is about computers and humans working together rather than machines replacing humans.  Augmented intelligence applications combine human and machine intelligence. This is important in systems there is low tolerance for error or where artificial intelligence is not evolved enough to take humans completely out of the loop.

That is essentially how continuous accounting, reporting, and auditing will be achieved.  Everything else is really simply a detail.