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.