Data Analytics in Internal Audit

As time passes by with advancements in technology, the audit function as a whole has been put in spotlight to upgrading itself in order to stay with the current trends. It is an inevitable part of evolution as to how the audit will function seamlessly. The key part of the data analysis is when the output of the activity is relevant to the needs of the stake holders. Can data analytics take over the all the activities that an auditor does? A BIG NO. Although machines have been able to replace the menial jobs that people have been performing for ages in the day to day routine, we still depend on the human brain for the decision making.

To make this happen strong governance frameworks are needed on data analytics, covering four areas: talent, quality, independence and security. It is also crucial to understand if the investment made in data analytics be worthwhile for the organisation to reap the benefits from it. The organisations have to arrive at a certain point where it would make sense for them to use the tools that is beneficial to them.

There is also a myth that data analysis is mostly for large organisations, whereas it is not. Even the smallest of organisation or the individuals per se can make the best use of it. Let’s say you are small organisation doing trading of vegetables, the use of the market rates from public portals such as NCDEX can help you assess the movement of prices over a period of time and also help you in assessing what could probably be the prices in the future on which you can leverage on. Let’s say as an individual you are interested in trading and you wish to understand the stock price movement, you can use the time series analysis for basic price movement predictions. All of these are only supplementary assistance, at the end of the day the money remains with the person with the ability to make the best judgements. Human mind is paid for the decisions it takes based on which the machines help achieve those goals defined by the human.

Now moving on the main topic: DA in IA

Effective analysis of data for internal audit is done only when the objectives are set clearly. As discussed above, to make this happen strong governance frameworks are needed on data analytics, covering four key areas: quality, talent, independence and security. To ensure that organisations are getting the most from their investment in analytics, they must review their governance frameworks with a view to adopting a more transformative approach.

Fast-evolving technologies that generate-increasing amounts of data have created an opportunity for internal audit departments to leverage data to evaluate risk and drive audit insight. As a result, data analytics is increasingly becoming an indispensable element of the internal audit toolset.

The need for data analytics

Analytics breaks down vast volumes of data and then rebuilds it to form information clusters that the auditor can use to analyse the risk landscape.

Effective data analytics elevates performance, provides greater value to the organisation, and increases the credibility of an internal audit with its stakeholders. It is also helping to transform internal audits by significantly automating processes, supporting compliance within existing organisational policies, and providing management with a higher level of operational assurance.

Conclusion

Data analytics is transforming audit by providing data-enabled insight coupled with automatic identification of high-risk items, allowing auditors license to prioritise and investigate high-value areas.

More importantly, a higher and unprecedented level of efficiency is achieved by letting analytics focus on transactional and low-value activities, with auditors focusing on high-risk items that require critical human observation and examination.

As well as the benefits in disrupting traditional audit processes, analytics also brings with it a number of inherent risks that can limit effectiveness or expose the department to reputational damage.

The starting point for managing these risks should be the careful review and development of a governance framework that helps align use of analytics to audit strategy and risk appetite.

The governance framework should articulate:

• clear roles and responsibilities in relation to the resources involved in the entire analytics process;
• address conflicts of interest issues that could potentially arise; and
• describe how issues will be resolved.

As data and analytics are key components in the evolution of internal auditing, the governance framework must then be incorporated in the organisation’s internal audit methodology.

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