Preliminary analytics should be more than just a workpaper that is required to finalize planning. It should be the foundations the entire audit is built from.
Exploring data and visualizations allows for a better understanding of your client, highlighting unusual trends and relationships and easily uncovering the areas the audit approach should focus on.
Preliminary analytics are a key opportunity to also plan for value. What areas of interest or concern can be identified through exploring the data and discussing visualizations with your client? These might not have audit implications but could provide value to the client if explored or reported on alongside the audit process.
Why are preliminary analytics important?
Preliminary analytics are performed to obtain an understanding of the client and to support scoping of work and the assessment of the risk of material misstatement. They allow you to develop a tailored, effective, quality and valuable audit approach.
What a current example looks like
Today, a real preliminary analytical review looks like this:
This review is adding little value to the audit process. The financial information is aggregated at a very high level so only a broad indication can be provided about whether a material misstatement exists.
This means client conversations are generic and imprecise answers are provided and documented. The audit approach is unaffected by this work, leading to general audit procedures being performed that do not focus on the key areas of risk, or where value could be provided to the client.
This activity is purely performed to satisfy the auditing standards requirement but fails to avoid the pitfalls detailed in these same standards.
Preliminary analytics becoming the foundations
Preliminary analytics can form the basis of a valuable audit engagement. An effective approach leads to:
- Better understanding of the entity and the financial results
- A highly tailored Prepared-by-Client list
- Detailed scoping of accounts and balances where a risk of material misstatement exists
- Granular assessment of the assertions relevant to each risk of material misstatement
- Robust consideration of where the risk of fraud is present
- Planning where audit procedures can be designed, or extended, to provide additional value
The result is a timely, well-planned, holistic audit approach. Valuable audit procedures are performed to address the specific areas where a material misstatement could exist and reduced or removed from areas which pose minimal or no risk.
What you’ll need: Data and timing
To remove the barrier of working with aggregated, high–level financial information, you’ll need access to transactional financial information. This enables visualising and exploring the data and ultimately answering your own questions.
To gain the maximum benefit, it is essential preliminary analytics are performed on interim client financial data. This allows enough time to discuss the results with your team and your client, appropriately planning the downstream impact of this work.
Steps in the process
The ten steps to valuable preliminary analytics are:
- Obtain interim financial information from the client
- Consider the reliability of the financial information
- Prepare side-by-side flux analysis and variance calculations
- Prepare ratio analysis and visualisations
- Identify key accounts and balances for investigation
- Obtain explanations from the client regarding key variances
- For each account/balance consider:
- Whether client explanations correlate to the story within the data
- Specific fraud risks identified
- Impact on scoping, risk assessment and overall audit approach
- Opportunities to add value during the audit
- Discuss results with internal colleagues, including other service lines
- Discuss results with client contacts
- Document results on the audit file and communicate impact to team
You can automate many of these steps using Inflo to transform your preliminary analytics today.
The result: What good looks like
Following this new approach to preliminary analytics will transform your audit services from the very beginning. This sets the tone for the engagement, showing your client a more collaborative, data-led approach and how technology is increasing the value of your services to them.
You can enhance your preliminary analytics by taking a foundational approach, more closely aligning to existing flux approaches, or transform the process through more advanced techniques, such as process mining and peer benchmarking.
Here are two examples:
Advanced Exploratory Analytics
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