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— Claire Worledge
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We can't be everywhere. But thanks to AI, we can train agents to pass along knowledge from experts to the rest of our team in a dynamic way.
However, sometimes we also just want to speak to The Human!
25 years ago, I had access to the information in the 300Framework, I would have been a star SAP auditor from day one.
Even 5 years ago, I would have saved months and months of time, had I had access to the e-learnings, masterclass and the 300Framework platform.
A data analyst spends about 80% of their time gathering and preparing data before it is AI ready or ready for use in audit tests. This time includes defining the data required, obtaining it and formatting it (filtering-out irrelevant records, converting quantities and currencies, adding descriptions, joining header and detail, resolving hierarchies, etc). You can short-cut this 80% thanks to the knowledge in the 300Framework.
But more than just the data, we also spend time, discussing which tests to do, understanding the relevant processes and risks, understanding the way SAP controls can be circumvented.
The 300Framework is about bring together knowlege about risk, SAP processes, business processes, data, python algorithms, data visualization, internal control and audit testing, so that your audit becomes truly effective.
It's 2001. I´m up in Birmingham for a training with the Data Quality and Integrity Senior Managers at Deloitte. They show us how to use CTRL+Shift+Arrow DOWN+RIGHT to select an entire dataset in Excel, create pivot tables and run filtering, ageing analysis, data comparisons in ACL. In the afternoon, one of the Senior Managers gives me a set of purchasing data and says "See what you can do with that".
To be honest, at that moment, my mind went blank. I actually had no idea what to do with the data.
Why did I have no idea what to do with it? I had a degree in Biochemistry, 2.5 years experience work experience in Nanjing, China and 2 masters in Information Technology. I had been at Deloitte for six months and I had followed the basic training about business processes, basic accounting and basic SAP and I had done a couple of audits.
Given the same dataset today, I would immediately know what to do with it. My brain would immediately list out all the supplier, purchase order, invoice and payment tests that I habitually do. A checklist mapped to all the fraud scenarios and risks would spring to mind. All of the data preparation steps and errors to avoid would pop-up like a roadmap of tasks. I would write out a quick Gaant chart of steps to be done in 5 minutes, and run through my standard set of python scripts -adjusting them as necessary - with the help from my best friends Claude and ChatGPT. I would set-up an automated reporting workflow and automated LLM report generation. I would then review the results, adjust analysis steps, check for mitigating controls, assign priority ratings and audit recommendations and produce a draft report. The next day I would go back to the Senior Manager and we would discuss the report.
But back in 2001, my mind was blank. I was "stuck" looking at my PC. No inspiration. I was lacking the knowledge. Without knowledge, we cannot have creativity. I was lacking the skills. Without skills, we cannot move forward. I was also lacking powerful tools. The less powerful the tools, the longer it takes to get the job done.
Recently, a senior auditor told me that he was a bit tired of re-explaining to every new audit recruit what a journal entry is, how to calculate customer margins, why purchase order value is not the same as the Accounts Payable value...
Sometimes it is not only the junior staff that are a bit lost though. Often we have very senior members of the team that get frustrated trying to explain what they need to the data analytics team. A senior finance auditor at a global manufacturing company could not understand why our dashboard about Accounts Receivable did not match the ageing report from the business. This was because I had made them a summary of transactions over time, rather than outstanding items at date. Both types of analysis can be useful. However, we need to be able to understand the difference so that the team is on the same page.
Another senior auditor at a global distribution company was reading our data analytics report and concluded that there was an error because the Accounts Payable value was the same as the expense value. We had to explain that the Accounts Payable value is inclusive of VAT. This then followed by a detailed discussion of journal entries and how journal entries are represented in the general ledger table as at least three rows, with the VAT and the expense values on the counterparty line.
When it comes to using data in our audits, there can be many interesting challenges. However, today, with the experience that we have and the new emerging AI tools, we can help audit teams get to the right answer quicker.
"My mission is simple: help auditors use SAP data to get the audit done efficiently."
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The methodology behind 25 years of SAP audit excellence.
by Aufinia