How Coca-Cola Hellenic and Credit Suisse are optimising internal audit using data analytics

The impact of data analytics on businesses across multiple sectors continues to grow, as innovative technology and workforce skills develop to ensure organisations are making the most of the information they hold.

 

Internal audit is no exception, and professionals are increasingly expected to leverage the latest advanced analytics techniques to deliver greater efficiency and effectiveness at lower costs.

Data analytics and internal audit in 2017

The latest PwC State of the Internal Audit Profession report, published in March, showed that 44 per cent of businesses in which internal audit’s role is crucial to anticipating disruption have increased investment in analytics.

 

These organisations, which PwC dubs Agile IA functions, are spending more money on analytics to drive risk assessment and continuous auditing processes. Forty-seven per cent of Agile AI functions are using data mining and data analytics techniques more frequently for trend monitoring and to spot potential disruptions.

 

Meanwhile, a new report from the Chartered Institute of Internal Auditors (IIA) has identified Coca Cola Hellenic and Credit Suisse as leading the charge in the battle to strengthen auditing performance through data analytics platforms.

 

Let’s take a closer look at how these industry giants are utilising advanced analytics.

Coca-Cola Hellenic reaps the benefits of ERP

Coca-Cola Hellenic is the primary bottler for the Coca-Cola brand, producing 50 billion servings across operations in 28 countries worldwide. The organisation has a sophisticated enterprise resource planning (ERP) system with massive quantities of data flowing through it.

 

Maximising the utility of this information was a key reason for the company’s audit function introducing a data analytics team, which is involved in designing ACL scripts and providing strategic contributions.

 

Richard Brasher, corporate audit director at Coca-Cola Hellenic, said data analytics are incorporated from the very beginning of the auditing process, from planning through to completion.

 

“The use of data analytics helps external auditors to rely on the work already done by internal audit and hence reduces duplication of time and effort,” he explained.

 

“Another advantage is that it frees up time, particularly during the fieldwork stage, but they try to use this extra time to make better use of the data in order to deliver a better-quality, more focused audit.”

 

The company can now test 100 per cent of the sample data, thus optimising the strength of its assurance processes. To ensure auditors are well versed in the technical aspects of the role, the organisation encourages staff to spend six months on secondment with the data analytics team.

 

However, Mr Brasher admitted that finding candidates with the right mix of technical and leadership skills for internal audit positions will continue to be a challenge in the future.

Analytics team takes Credit for Suisse audit success

Global private bank Credit Suisse considers itself at the advanced end of the data analytics maturity path, with the organisation incorporating the technology organically over the years alongside other innovations.

 

The bank believes adopting analytics has enabled it to transform from a judgement-based, sample-driven and largely manual audit function into one that offers a risk-based, continuous and data-driven approach.

 

“Data analytics is helping the organisation to identify business areas with high-control risks due to anomalous, non-conforming events, and is facilitating the continuous monitoring of the risks,” said chief auditor of regulatory and people risks Mark Starbuck.

 

“Data analytics are used in the entire internal audit cycle but mostly as part of fieldwork. An example is management oversight of cross-border risk, where analytics was used to test the hypothesis that policy breaches have occurred and are undetected.”

 

Credit Suisse is also focusing more effort on continuous risk monitoring in 2017, with increased emphasis on planning, fieldwork and reporting.

 

Mr Starbuck noted that the data analytics team has been successful due to sponsorship and buy-in from internal audit leaders. There continues to be strong advocates for data-driven methodologies, with training and awareness programmes helping to deliver the necessary skills to perform analytics and use core applications.

 

“The ideal data analytics auditor has a blend of core analytics skillsets, business functional experience and a good understanding of risk,” he explained.

Finding the right talent

The IIA case studies show the benefits of data analytics within the internal audit function. These include:

• Increased efficiency via the re-use of scripts for periodic audits
• Improved effectiveness through whole-population testing
• Enhanced assurance
• Time and cost savings
• Greater focus on strategic risks
• Broadened audit coverage

Nevertheless, finding people with the right mix of skills remains a challenge for many businesses hoping to maximise data analytics use across the audit function.

 

Earlier this year, our research revealed that demand for IT auditors with data analytics and data assurance capabilities has been on the rise, with senior-level staff particularly sought after.

 

With organisations placing more importance on data analytics within internal audit, we expect this trend to continue for the rest of 2017 and into the years beyond.

 

Our Market Reports combine our review of the prevailing conditions in the internal audit recruitment market together with the results of our latest employer survey.

 

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