Auditors today are working in an exceptionally complex and ever-evolving industry. In an environment where there is pressure to add more value with fewer people and less time, auditors are challenged to discover more risks, audit more areas, and uncover fraud for their clients.
Yesterday − ineffective audit planning methods
Historically, audits have often been conducted at planned intervals rather than based on thoughtful consideration of evidence that point to patterns or areas of risk. Auditors usually selected a given area and assessed risk areas based on consultation with the organisation. Depending on responses, audits would either be performed annually or every two to three years for internal controls, if deemed to be less risky. Unfortunately, this method can facilitate overlooking risk areas and permit fraud or other issues to go undetected.
Today − effective audit planning methods
To help improve the effectiveness of audits and clearly demonstrate their value, auditors can deliver more value for their clients by firstly optimising audit planning and secondly, empowering teams with systems and tools that focus subject matter expertise on the audit, as opposed to spending unnecessary time in extracting information from systems and preparing data within spreadsheets. Thirdly, it is important to pinpoint findings that will save the company money and improve its business operations.
A more systematic approach to audit planning offers a variety of advantages, including:
- Breaking risks down into individual factors and then being able to align them across the audit landscape
- Individually scoring risks for each financial statement area
- Calculating an overall score but also ensuring that specific levels of risk are clearly reflected on financial statements
- Evaluating relative risk levels in order to establish the audit scope
The data analytics approach to audit planning
It is well known among auditors that data analytics helps to simplify and improve audits, eliminate manual tasks, reduce costs, and detect potential fraud, errors and abuse – and, most importantly, these valuable insights are attained earlier in the process. However, many auditors are missing the opportunity to leverage data analysis software to help prepare a rounded and well-thought-out audit plan. Using data analytics during the audit planning phase helps to focus audits, allocate resources effectively, optimise audit expertise, save time, and identify important information about the business.
Audit planning – get it right with data analysis!
Running data analytics tests during the audit planning phase helps to provide a better understanding of what is happening and highlights the areas of greatest risk. Moreover, it shows where control breakdowns are occurring and the state of risk management in the company. Utilising insights provided by data analysis in this phase of the audit process helps shift or refine the focus of the audit at an early stage. Also, if used in the planning process it can provide auditors with a comprehensive understanding of the scope of the business and can help to identify specific questions that need to be put to the company to adjust the focus of the audit – as and where required. Moreover, data analytics helps to automate a significant number of manual tasks and save considerable time and costs. What can often take days to complete can be executed within minutes when the audit commences.
What is the best approach to building an audit plan?
Firstly, complete a risk assessment − but where to start? Trying to analyse each area can be time-consuming and overwhelming. Instead, the best option is to start with a few key areas at a time and the ones that reveal the most problems are the ones that should be audited first. Testing can then be expanded from there.
THE 5 key areas to test!
Data analytics applied to the following five key areas will reveal potential risk – an audit plan can be built based on the information revealed from scrutinising the following:
1. Accounts Payable
2. Accounts Receivable
3. General Ledger
5. Stock and Inventory
The value proposition of advanced data analytics
Traditionally, auditors have depended on IT departments to analyse data and provide reports to answer critical audit questions. The challenge is that it requires continuous access to power users with expert knowledge, within the client company. Additionally, incorporating multiple data sources across the business has been technically complex and low system performance resulted in long wait times between queries.
However, auditors today should become the analytics experts giving them control over their analytics queries and reports. Advanced data analysis tools help auditors to strategically plan their audits and lead to better, more effective, audits. Benefits include:
Increased audit scope by allowing imports of all records, including spreadsheets, and exported data from various databases, accounting software, ERP systems, travel, and entertainment applications in formats such as PDF, plain text (.txt), print-report (.prn) and open database connectivity (ODBC)
- Automation of the sampling processes by eliminating the need to export data into spreadsheets and preparing manual sample set
- An analysis of 100% of the data across a multitude of datasets
- Creation of an audit trail as the analysis is performed so that the various steps can be documented, shared, and repeated
- Creation of repeatable tests to reduce time spent on routine tests
Some important factors to take into consideration when evaluating and selecting an audit analytics tool are as follows:
Universal file conversion capabilities that make it fast and easy to import all records
- Advanced audit functions with just a click, including a multitude of tests such as Gap Detection, Benford’s Law, fuzzy duplicate matching, summarisation, stratification, and various sampling options
- Data visualisation with the ability to either auto-populate or custom-build dashboards with charts and field statistics into which auditors can drill down
- Customisation through scripting platforms with built-in support for languages such as Python