Just a few years ago, artificial intelligence (AI) technology used to be the subject of science fiction and futuristic movies. Thanks to rapid developments in computing technology, this fiction has become a reality affecting every aspect of our professional and personal lives. New software options are now transforming internal auditing.
Internal auditing (IA) is a multi-faceted process, in which experienced professionals evaluate a business independently. It is a systematic approach to financial reporting and assessing the business culture, processes, and systems used by a company. Based on their analysis, they evaluate opportunities and risks arising from global issues or emerging technologies, for example.
Auditors may suggest putting into place internal controls and other risk management measures. The goal is to ensure effective governance that ensures compliance with laws and regulations as well as meeting business objectives.
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Contemporary Internal Audit Challenges
Effective internal audit activities rely on data analysis. Whilst human auditors are certainly capable of performing these analytical tasks, technologies like AI are simply better suited to those tasks. AI is capable of analyzing huge amounts of data in less time and with more accuracy than humans. The technology uses algorithms to understand patterns and faults in data sets. Moreover, artificial intelligence software can check various future scenarios and predict which path is most promising.
The goal is not to take over from human auditors. Instead, AI delivers the groundwork on which audit firms can base their suggestions. Rather than spending their time completing repetitive analytical procedures, auditors join the process at the next stage, using the results of AI analysis as the basis for their decision-making. Businesses benefit from reduced audit response time.
Integrating AI allows internal audit teams to address several common contemporary financial audit challenges. Consultancy firm Deloitte believes that internal audit tasks have changed dramatically over the past few decades. Businesses are no longer seen as islands. As many are outsourcing even key business functions, it has become harder to complete an internal audit process because the process extends far beyond business premises.
Businesses have less oversight over their contractor’s practices, and auditors often face a complex environment of fourth and fifth parties when they are completing their analyses. As a result, completing an audit can take months.
Multiple external threats add further complications to the process. Most businesses currently deal with one or more of the following issues: lingering effects of the pandemic, labor shortages, climate change uncertainties, cyber threats, disrupted supply chains, and political or social unrest. All these factors have made it more difficult for auditors to create an accurate picture of a company’s operational risks.
AI algorithms are well-suited to explore correlations between these individual factors and the potential risks they cause for a business. They can review a range of different combinations fast and evaluate the likelihood of each confluence of circumstances. As those circumstances change, it is also relatively straightforward to adjust algorithms and their predictions to gain a more accurate idea of the challenges they pose to the business.
Making AI Work in IA (Internal Audit)
Bringing artificial intelligence software into an AI audit requires careful consideration. The algorithms’ predictions are only as good as the data sets an organization can supply. Human auditing teams may also have reservations about robotic process automation and handing over part of their work to machines. They need to understand that the goal is not to replace them but to enhance their work.
Overcoming these challenges for a successful deployment of AI requires a strategic, well-planned approach and consistent communication throughout the organization. Communications ensure that the audit team understands that the company is not aiming to replace human intelligence. Algorithms and AI in general will be deployed to deal with onerous, manual tasks and use auditors more efficiently.
Audit teams need to understand the benefits of integrating technology into their normal work processes. Team members may also need reassurance that their jobs are not suddenly disappearing. By focusing on those issues, leadership teams secure buy-in and efficient use of the technology in place.
When it comes to ensuring data quality, businesses need the input of data scientists. For most companies, that means engaging with external consultants who can build an effective data infrastructure. In this context, being effective refers to data that can continuously take in new information to stay up to date without requiring constant human support. Setting up a system that can react to changing regulations, economic challenges, and market conditions is critical for the successful implementation of AI in the IA process.
Machine learning (ML) algorithms are well suited to this task. They have been programmed in a way that allows them to learn from their own results and integrate those learnings into their predictions going forward. Deep learning takes this approach further.
For many businesses, implementing AI means working with external data science contractors to introduce a productive data infrastructure into the audit environment. The transition may also require a degree of change in the overall business culture.
AuditMap.ai: A Platform for Audit Enhancement
One of the most effective AI-powered tools for internal auditing currently available is AuditMap.ai. This platform creates a dedicated audit space and allows users to start making risk assessments instantly. Plus, the software also makes it easy to be ready for recurring internal expense audits. As a consequence, IAs are no longer a dreaded long-term undertaking. Instead, they become an agile business tool.
AuditMap works by reviewing internal reports and other documents to develop a risk profile. This includes highlighting any unmitigated risks. This powerful tool also picks up trends before they can pose a risk, allowing your organization to develop an effective mitigation strategy. Options to sort results by type, date, or year give auditors a better understanding of recurring or seasonal risks.
Moreover, artificial intelligence solutions like this tool assess the efficiency of existing controls and procedures, helping you identify gaps that need to be closed. AuditMap works as a cloud-based internal audit function tool, but it can also be deployed on a company’s premises or in a hybrid version. The goal is to offer users high levels of document safeguarding that suit their circumstances. Its proprietary AI algorithms are based on state-of-the-art natural language processing (NLP), which is highly effective in the assessment of risks, controls, and objectives in combination. NLP is helping machines understand the intricacies of human language.
Preparing audit resources used to be a time-consuming exercise. With the help of AI-powered auditing platforms, this task is limited to uploading existing audit reports and pertinent documents for the AI to consider. Organizations can choose to wait until their next audit or decide to incorporate uploading into their daily business processes. The latter tends to work better for many businesses as it avoids any last-minute rush to gather information.
Limitations and the Way Forward
Whilst experts agree on the potential of AI in internal auditing, adoption has been slow to date. Potential reasons for this delay likely include a lack of budget, cash flow challenges, and internal push-back around the perception that implementing AI will cost jobs.
Overcoming these challenges relies on improving the understanding of the advantages AI brings to the IA process within the field. AI allows auditors and their employers to consider every transaction as opposed to limiting themselves to larger ones. As a result, the insights gained will be more accurate, avoid incorrect amounts, and lead to better risk mitigation which will lead to lower operational costs in the mid to long term.
Is there a limit to the benefits of AI in IA? That question is hard to answer in a field that is developing so fast. One of the current limitations is the lack of standardization in AI development. Standardization tends to advance the implementation and adoption of technology simply through common foundations. In addition, effective standards also improve the marketplace for applications because systems become more compatible.
Another consideration and potential limitation of AI implementation is push-back from employees at different levels. Not many auditors are data scientists. Therefore, their understanding of AI and what constitutes an effective data infrastructure may be limited, leading to unfounded concerns. Companies looking to implement AI would be well-advised to consider appointing audit committees to liaise with internal audit teams. Another step could be offering training and familiarization programs early on in the transition process.
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High-quality audits are crucial for accurate business decision-making that allows organizations to manage financial risk and reputational risk, among others. Implementing AI in the process helps streamline audit procedures, leading to improved audit quality.
For a successful transformation process, companies need to understand employee concerns and treat them seriously. Whilst implementing AI in IA will cause upfront costs, the transition soon pays for itself through better risk mitigation across the organization.