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Common Uses of Data Analytics by Internal Audit & Benefits of Using It

Common Uses of Data Analytics by Internal Audit & Benefits of Using Data Analytics

Most commonly, auditors use data analytics for fieldwork and engagement planning, and use the results to identify anomalies and test controls. For many functions data analytics is used in more financially oriented audits such as: General ledger; Purchase to pay; Payroll; Travel and subsistence/entertainment; and order to cash (a set of business processes that involve receiving and fulfilling customer requests for goods or services).


Perform supplier audits by utilizing line-item billing data to identify anomalies and trends to investigate. Identify poor data quality and integrity around various data systems that are key drivers to non-compliance risks. Identify areas at high risk of fraud and assess controls. Isolate key metrics around spend analysis e.g., payment timing, forgone early-payment discounts and payment efficiency. Perform duplicate payment analysis and recovery.

 Identify key performance and key risk indicators across industries and business lines. Detective and corrective e.g., control account reconciliations. Compliance Fraud, risk assessment, detection, and investigation. Analytics can enable internal audit to automate the more routine activities of the internal audit process, which then frees up time to do deep dives on the more strategic and complex issues. Internal audit needs a detailed understanding of the potential benefits of data analytics before implementing it in audit processes.
 The key benefits include: Increased efficiency. For example, scripts can be re-used for periodic audits resulting in efficiency benefits by avoiding repeated manual analysis; Increased effectiveness. Analytics allows for whole population testing instead of random or judgmental sampling, as well as enabling continuous auditing so that internal audit and the business can pick up on emerging trends and themes and be more nimble with their risk monitoring.
Improved assurance, for example, analytics reduce the margin for human error in the analysis of vast data sets and allow for greater precision in assessing operational performance; A greater focus on strategic risks by moving away from the more routine tasks which can be automated to a greater degree; Greater audit coverage; and Significant time and money savings over the longer term.

Credit: Data analytics – is it time to take the first step? by Chartered Institute of Internal Auditors (April 2017)


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