What should your organisation expect when it engages an auditor?
In addition to financial statement preparation, audit accounting services should certainly include compliance checks and opinions, risk alerts and risk assurance, advice on improving systems and processes, and a corporate governance and probity review.
This can be a complex and lengthy process, involving the inspection and analysis of large amounts of data. Fortunately, efficient and forward-looking audit firms now use machine learning to assist risk-based audit methodology, reducing time spent on repetitive tasks and thus containing costs while improving service quality.
What is machine learning?
Machine learning is a branch of Artificial Intelligence (AI) focusing on the use of data and algorithms to emulate the way humans learn, with increasing accuracy over time as more data is processed. Like the human brain, machine learning relies on neural networks, although in this case they are artificial and lodged in a computer. Multiple connected node layers are used to process data, pass information between layers and calculate the final output.
In this way, machine learning automates analytical model building, understands patterns and makes predictions. This makes possible deep analysis of ‘big data’ within a short time frame, rather than the representative data sampling typical of legacy auditing procedures hampered by time constraints.
How machine learning can be used in auditing
Machine learning has multiple applications in auditing. It is already used by leading accounting services firms, and new functions are constantly being introduced. Some of the current machine learning audit practices include:
- Automation of manual audit tasks
- Analysis of the complete volume of data – not just a sample – including all sales and purchase records, general ledger and all other ledgers, journal entries, bank transactions, financial reports, authorities and limits, to flag transactions that differ materially from the standard
- Identification of exceptions, and potential problems or errors, including duplicate expense claims, unauthorised expenditure, incorrect amounts and suspicious suppliers or invoices
- Reading of contracts and leases to pinpoint key clauses and numbers, assess risk and highlight any anomalies
Many accounting firms and academics are already studying additional ways that machine learning can be used in financial statement audits, particularly in the risk assurance process.
In the future, machine learning may be used in fraud detection to identify speech patterns and facial expressions that suggest nervousness or deceit. It may also be possible to assess non-financial metrics such as location images and weather patterns, and analyse their likely impact on factors such as revenue or spoilage.
The benefits of using machine learning
Human auditors, released from repetitive and time-hungry tasks, have more time to use their cognitive skills to focus on review, interpretation, evaluation, systems improvement and risk assessment. Their advisory role comes to the fore, instead of being submerged in a welter of manual data processing.
As a result of the coupling of machine learning and human expertise, the audit quality is improved and it produces more accurate outcomes and predictions without automatically increasing the cost to clients.
Bentleys understands the importance of machine learning in auditing
Both the External Audit and Internal Audit teams at Bentleys are thoroughly conversant with the application of machine learning to the auditing process. We can explain how it will benefit you by both keeping costs down and allowing us to focus on what we do best, including understanding and managing your business and technology risks, assisting you in formulating improved procedures and controls, providing compliance opinions, reviewing your corporate governance, and, of course, preparing financial statements.
We can help
Contact your local Bentleys advisor today for a no-obligation assessment of how machine learning can benefit your audit.
Disclaimer: This information is general in nature and should not be relied on as advice. It does not take into account the objectives, financial situation or needs of any particular person. You need to consider your financial situation and needs and seek professional advice before making any decisions based on this information.