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Internal audit sees increased demand for data analytics

In line with the increased focus on corporate risk management and the growing use of data analytics across the financial function, 66 per cent of internal audit functions now employ data analytics in audit processes.

Developments in technology and tools that enable more sophisticated data analysis are increasingly being employed to manage financial and treasury processes, from cash flows and risk indicators, to governance, compliance and reporting. It's no surprise therefore that two-thirds of internal audit functions now employ some form of data analytics in audit processes, according to a survey by business consultancy firm Protivi.

Advanced data analytics is the future

The survey of more than 900 internal audit professionals found that internal audit functions face numerous challenges, including:

  • 73 per cent of all companies performing analytics say demand for data analytics has increased – even more so among companies that engage in best practices;
  • 34 per cent are planning to add analytics headcount;
  • identifying where data resides is a challenge for 60 per cent of organisations as are system constraints (56 per cent);
  • data quality is an issue as well – only 22 per cent rate it to be excellent or good.

Protiviti's Brian Christensen said: “Internal audit professionals must be adept in applying new tools and techniques to understand and manage risk. The use of advanced data analytic techniques is the runaway winner as a best practice of the future.”

Data analytics best practices

The report looked at the challenges facing the internal audit function within companies. It found that internal audit groups with dedicated analytics functions and organizations that have attained a ‘managed’ or ‘optimised’ state of analytics maturity are far more likely to conduct continuous auditing. It also identified some of the requirements for overcoming constraints in building sophisticated analytics processes, which include:

  • a longer-term strategy and an implementation roadmap;
  • carefully chosen and well-crafted pilot programmes;
  • a clear direction (including investment in skills, tools and expertise) from chief audit executives (CAEs) and organisational leaders.

Christensen also noted: “It can be overwhelming for organisations just getting started with using data analytics. There may be budget and resource constraints, employees need to learn new technologies and new processes need to be developed. We’ve found that companies just need to pick a starting point and get the help they need so that, over time, they can truly optimize their internal audit functions.”

Risk of misleading data

However, a report from the Institute of Internal Auditors (IIA) yesterday noted that auditors need to pay careful attention to internal audit's use of data analytics, particularly with regards to the relationship between internal audit and its clients. The IIA's report found that 66 per cent of internal audit leaders have concerns regarding reputation risk due to faulty communications. The IIA report also found that, in contrast with Protiviti's findings, 95 per cent of internal audit functions use data analytics, while only 22 per cent use data analytics for risk assessment in developing the department audit plan.

This item appears in the following sections:
Control & Compliance in Operations
Operational Risk Management

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