Continuous Assurance with data analysis

After laying out the webinar’s house rules, Neil sets out the content for this webinar. Then both Alain and Neil introduce themselves as well as their respective businesses (Sepia Solutions and Technology4Business).

Neil explains how we define “data analysis” and why it is so important for managers as well as assurance teams. He also illustrates the value of data analysis with practical applications and discusses the data landscape.

What are typical limitations of a traditional audit, or standard assurance approaches? How valid is a sample, really? How frequently is the data analysed, for which periods and how long after the facts? How vigilant does management remain in between reviews?

Neil highlights the differences between a traditional review and continuous monitoring of processes. He underscores the significant advantages of the continuous assurance derived from the frequent and automated analysis and while also illustrating who benefits.

What are common excuses for not doing continuous assurance? Perhaps a lack of tooling, resources, or know-how? Is your data accessible, valid and clean? Is the analysis available to all 3 lines of defence? Or is any solution considered to be too expensive?

Neil explores two possible paths to implementing Continuous Assurance, each with their own characteristics. In the more traditional or “incremental approach” review tests are automated and expanded over time. On the other hand, the “tactical wins approach” delivers results far more quickly.

Neil considers pricing for both the “incremental approach” and the “tactical wins approach” with realistic budget figures. Not only focussing on costs, Neil’s arguments also touch on the ROI (Return on Investment) and turnaround time of a project as well as the cost of not doing it (quickly).

Alain sets out 3 types of controls in a process:

A. Recalculation of totals
B. Duplicate invoices
C. 2-way matching

The main message is the approach, not so much the business process or even the specific (and very simple) controls. Intead, the key point is how you can build and automate your own tests in the Arbutus Analytics platform.

Neil summarizes the session by stressing the benefits of continuous assurance and how even a small team can have spectacular impact on the organisation. Can one even afford, really, not to use data analytics on a structural basis?

We close the session with the classical Q&A, addressing questions like “Can Arbutus extract data from SAP?” , “Can Arbutus connect to ERM systems?”, “What if the tests have already been done on the 1st and 2nd line (of defence)?”.