Auditor data analytics is about enhancing audit quality. There are different angles on what this means in practice but audit quality is a common objective of auditors, regulators and standard-setters alike. A high-quality, focused and effective audit is aligned with the way the audited entity manages its data and operations. Data analytics offers a practical way for auditors to manage some important aspects of IT systems in larger audits. Competitive tendering for listed company audits has sharpened the focus on data analytics, and audit committees now routinely ask prospective auditors how they are going to use it in the audit.
Innovation within regime
The profession has an opportunity to reinvent itself within an existing and mature regulatory regime. Regulatory change necessarily proceeds with caution but innovation in audit is essential. Without it, the ability of the profession to respond to market demands will be compromised and there is a risk that the external audit itself will be marginalised. This is a debate about what business and investors really value in audit and, in the light of the opportunities data analytics presents, how that might be achieved.
Improving Audit Quality
Data analytics enables auditors to manipulate an entire data set not just a sample, 100% of the transactions in a ledger and interrogate the relationships across many multiples of ledgers from all across the world at lightning speeds. Non-specialists can then visualise results graphically, easily, and at speed. Modern Data Analytics enables efficiency, it’s about getting to the things that matter quicker and spending more time on them instead of ploughing slowly through random samples that often tell you very little. These techniques shrink the population at risk. It means fishing in a smaller pond and getting straight to the high-risk areas delivering faster forensic accounting.
Data analytics enables auditors to improve the risk assessment process, substantive procedures and tests of controls. It often involves very simple routines but it also involves complex models that produce high-quality projections.
Commonly performed data analytics routines
• Comparing the last time an item was bought with the last time it was sold, for cost/NRV purposes.
• Inventory ageing and how many days inventory is in stock by item
• Receivables and payables ageing and the reduction in overdue debt over time by customer.
• Analyses of revenue trends split by product or region.
• Analyses of gross margins and sales, highlighting items with negative margins.
• Matches of orders to cash and purchases to payments.
• ‘Can do did do testing’ of user codes to test whether segregation of duties is appropriate, and whether any inappropriate combinations of users have been involved in processing transactions.
• Detailed recalculations of depreciation on fixed assets by item, either using approximations (such as assuming sales and purchases are mid-month) or using the entire data set and exact dates.
• Analyses of capital expenditure v repairs and maintenance.
• Discover relationships between purchase/sales orders, goods received/despatched documentation and invoices.
Mergers & Acquisitions (M&A)
The increasing use of data analytics has made it a powerful tool throughout the merger and acquisition (M&A) deal lifecycle. The insights gained from data provide for a deal-making advantage, especially during the integration stage. Analytics may help unearth potential risks and hurdles to successful integration and post-deal execution.
Intellectual Property Intelligence
The use of automated natural language processing to assess the intellectual property of an acquisition target and then to cross-reference those findings with other databases aids an acquiring company in evaluating the stability of the intellectual property of an acquisition target, and can help avoid potential litigation or regulatory pitfalls.
Another powerful application is the use of deep data to analyse the talent pool of a potential target. In fact, using analytics to better understand the target’s workforce and compensation structure is the second-most frequent application of data analysis in an M&A situation according to Deloitte.
Where talent is often the most significant asset of any organisation, it is critical for any deal maker to understand who is working, who is managing, and who poses the greatest likelihood of leaving after a deal is consummated. Data Analytics can often reveal if the ratio of managers to employees is out of proportion, or if the compensation structure of either firm is far different than the other.
Advanced analytics open a window into any workforce, helping to reveal critical demographic features, patterns of employment, and potential risk factors. The potential for talent flight is often overlooked in a transaction, but thanks to analytics, that risk can be better understood and possibly accounted for in advance talent management strategies.
Contracts & Textual Analytics