Applying data analytics to improve audit and compliance functions
Data analytics is a vital tool enabling finance industry leaders to implement a risk-based approach towards internal and compliance audits and to determine areas of improvement when certain issues are detected.
The pandemic has created several challenges for many finance teams, particularly in regards to corporate compliance and internal audit functions. Despite a transformation in how we work, the structure for compliance and internal audit professionals hasn’t changed.
The primary aim continues to be identifying and managing risks to the business. To effectively implement this, businesses must continue to enhance their compliance programs, update their risk assessment and alter their audit plans and priorities. Compliance and audit functions need to demonstrate assurances to business leaders and show they have taken the necessary steps to manage potential risk. Compliance and audit teams need to understand how to implement efficient compliance and audit testing processes in this new environment. The most effective solution will depend on the business, their risk profile and other related variables. Data analytics continues to be an effective tool for compliance and internal audit professionals and has proven to be valuable in ensuring the correct measures are in place to manage fraud and other abuse.
Data analytics enables businesses to implement a risk-based approach and prioritise their focus on areas where issues may develop. Businesses that adopt a random sampling approach rarely provide the insights required or a meaningful audit result. Before the pandemic, data analytics was continuing to deliver a clear value in terms of compliance efforts. Today, compliance and internal audit functions are having to do more with less and so it is critical to understand how to leverage data analytics to your business.
Remote testing has become a normal procedure and is likely to be a common practice for the foreseeable future. Gathering, assessing and measuring important datasets to deliver compliance or internal audit processes is important.
In some cases, some data sets may be used to examine high-risk transactions. The internal audit team can utilise this information to deliver the most appropriate audit system to reach the required outcome.
Data quality is the most important element of data analytics. Providing direct access to vital databases can reduce potential issues of using inaccurate data sets. It enables compliance teams and internal auditors to check the accuracy of the information by working closer with IT teams. Direct data access also decreases the time to implement testing processes as other members are not required to work in these stages i.e. the initial data collection process. Through this process, compliance professionals and internal auditors can learn data limitations by engaging with the relevant IT professionals and then evaluate what impact this will have on their project.
One of the most vital elements of data analytics in regards to compliance and internal audits is the potential to combine disparate data sources. Many data analytics tests based on vital information can be carried out to determine risks. Generally, this information will be contained in disparate data sources, so having a clear understanding of those data sources and being capable of connecting the information is essential.
A risk-based approach vs. random testing
Data analytics allows compliance and internal audit teams to pick key transactions and test via a risk-based approach, rather than selecting transactions by random. A risk-based approach demonstrates how a business is managing risk effectively.
Defining the parameters for data analytics tests requires some work initially. Creating an understanding of system databases and data structure may take time to develop. However, each compliance assessment or internal audit will benefit from these plans and future work can be based on these tried and tested parameters. The repeated element of this process provides efficiency and enables a process that a data analyst can expect to see the same or similar results.
With remote working likely to remain dominant for the future, the conventional technique of random sampling and onsite testing will inevitably evolve into one that focuses more on remote risk-based auditing processes. Businesses need to leverage data analytics to effectively manage the challenges facing corporate compliance and internal auditing.