The impact of automation is visible in most industries. The auditing industry is not an exception. With multiple recent developments in technology and data, the audit industry is witnessing a surge in automation. The interruption of advanced technologies in the audit process is essential since some auditing tasks consume a lot of auditors’ time. Some of these tasks are prone to errors; some are repetitive, while some are just rule-based mechanical tasks, which can be easily completed.
Therefore, the auditing industry must explore the usage of new technologies in its processes and procedures. The key technologies that have shown implications in the external audit process and internal audit process include artificial intelligence, cloud computing, data analytics, robotic process automation, and many others. These advanced technologies affect the auditing process as well as the auditing firms’ business models. They give a new dimension to the numbers, from which it will be easier to derive new strategic insights, which will help in decision making for the auditing firms and their clients.
These technologies give the auditors a better view of the organization and help them in the manual processes. Nonetheless, the significance of auditors to the auditing process cannot be undermined. Their knowledge-based judgment, business and risk acumen, and analytical and critical thinking have no replacement. The auditors are important for the businesses since they identify the inefficiencies in the financial statements and give their opinion on the businesses’ financial situation.
Benefits of audit automation
The advent of audit automation has brought several benefits to the audit industry, some of which are enumerated as below:
- Sophisticated analytical tools enable auditors to study and analyze large amounts of the company’s data to gain deeper insights and analytics into their operations. This helps in transforming the basic premise of auditing, which focused on the audit of a data sample previously to the testing of the full data sets now. Companies can focus on increasing the frequency of auditing or a continuous audit model so that business is inundated with continuous insights on operations. This ensures an increase in assurance services, expansion in coverage, timely insights, and better reporting, thereby leading to higher business value.
- Replacement of manual processes with an automated audit procedure ensures completion of audit steps at a heightened speed and generation of continuous insights. This reduces the costs for businesses and increases the efficiency of business processes.
- Auditors can keep a continuous track of the business trends and monitor the processes to scrutinize high-risk operations. Since a continuous auditing process ensures uniformity and efficiency in results generation, errors are easily identified and rectified. Therefore, the accuracy of error detection improves, and the quality of audit reaches a higher mark, leading to a higher quality output for businesses.
- Since the technologies make the processes easier and faster, auditors get more free time, focusing on non-standard processes that require judgment. They can focus more on having quality interactions with the client teams, improving the quality of assurance reviews, generating more insights, and recommending strategic decisions. With the key time spent on such value-generating activities, the operational effectiveness of the business improves.
We understand the key benefits that these emerging technologies bring to the table for the audit process, but what technologies bring the big change? How are they making the processes more efficient? How are the auditors affected by the integration of these technologies in the audit process? How does the future of audit look with the advent of these technologies in the audit space?
Key audit automation technologies
The key technologies that ensure audit automation and make the audit function more efficient and effective are as follows:
Artificial intelligence (AI)
Artificial intelligence is the simulation of human intelligence in machines to enable them to think and work like humans. The key objective is to make the machines perform intellectual tasks that humans do, such as solving problems, making decisions, understanding others, and similar tasks. AI is also called cognitive technology involving algorithms that make it similar to human processing.
AI enables risk-based assurance services, which was earlier possible through the traditional way of defining rules. The earlier method’s problems were the likelihood of missing items or exceeding the timelines because of the involvement of human judgment. However, risk-based assurance services deconstruct data to compare it with standard data and identify the deviations. Furthermore, it is possible to analyze large amounts of structured and unstructured data since the AI system adapts to more data over a period and detects any correlation.
Another thing that AI has improved is the ledger understanding and analysis process. With such detailed analysis, auditors can explore further to generate more details so that they can present a bigger picture of the client’s financial situation. Furthermore, AI reduces the client’s constant reference for asking any questions related to ledgers, thereby leading to an efficient process.
By processing and analyzing a large set of structured and unstructured data, AI enables the auditors to judge a deeper and broader set of data and form detailed evidence and insightful analysis for audit reports. This improves the auditors’ capabilities to deliver highly valuable and insightful audits to the clients and make decisions regarding risk management.
Remote work also provides the auditors extra additional time saved from traveling to the office daily. They can use this time saved in having fruitful interactions with the clients regarding the key business aspects, leading to better strategic decisions for them.
However, auditors must ensure that cloud technologies are effective enough to make the process efficient and confusing. Specifically, they must focus on the speed and agility of such an interface so that no delays happen to the audit process. Another key factor is that the configuration of cloud technology must be such that it can support any possible future innovations so that no extra work is required in their integration. It must not increase the complexity of the audit process. Lastly, the heightened cybersecurity risks involved in cloud technologies are a cause of concern. For this, auditors must select the right cloud provider who can provide effective risk mitigation strategies to protect the highly confidential data and information present in the cloud.
Robotic process automation
Robotic process automation (RPA) works together with other application software at the level of the user interface. It is similar to the process of human interaction with the application software. RPA can automate the business processes, which the user pre-defines and executes across different software applications. These business processes must be structured, based on rules, and repetitive.
RPA has implications in many business processes, such as human resources, operations, and finance. Even in the audit process, one can deploy robots to automate tasks like copying and pasting financial information and reconciling financial information. RPA is put to the best use in generating insights from unintegrated data sets present in different systems. Generally, audit automation in the past involved automating each step of the audit process in isolation. For example, the audit process involves using the inputs to generate outputs that can lead to analysis and insights for the user. The audit automation involved:
- Automation of the data sourcing procedure to reduce the time and effort of auditors in the collection of data
- Automation of the process of combining different data sets into one single data set to make data assessment possible
- Automation of the coding framework that tests the data and transforms the input data into output insights
Automation of each step of audit in isolation, as mentioned above, is possible through emerging technologies. However, to obtain the maximum value from the audit procedures, it is essential to integrate these isolated automated audit steps into one single end-to-end automated audit process.
RPA allows the auditors to think through the steps of the audit process, redesign any inefficient steps, and reorganize the steps for better results. RPA facilitates the key steps of the audit process, including confirmations, reconciliations, email generation, and automation of emails. Specifically, the data analytics performed on the audit evidence identify the risks to data sets and the requirements in risk assessment procedures. Therefore, for any repetitive, structured, rules-based, and standardized audit processes, RPA must be applied to make them efficient and effective for auditors to make decisions.
Predictive analytics uses multiple statistical techniques to predict the future. It uses a combination of data, statistical algorithms, predictive modeling, and machine learning techniques to study and analyze the past and present data to make predictions about future events or unknown events. It generates a list of the most probable outcomes and their related probabilities.
Auditors use predictive analytics to identify patterns in data sourced from the company. Using this technique, auditors collect data from the company, extract relevant information, and identify the possible patterns. These patterns allow the auditors to understand the risks to a company’s operations regarding financial risks, operational risks, or any other risks.
These patterns may or may not represent the outcomes generated; however, they help in better assessment of risks to the client. The comparison with the industry data or data from other companies in similar situations gives an insight to the auditor regarding the client company’s probable situation in the future. Therefore, the quality of the audit process improves as it considers the comparative paradigm to generate insights.
Intelligent Process Automation (IPA)
Intelligence process automation combines most of the technologies mentioned above to make the audit process more scalable, flexible, and, specifically, more intelligent. It is RPA, but more intelligent rather than mechanical. It is an ecosystem of multiple technologies, including RPA, AI, data analytics, internet-of-things, etc. It is used to automate the processes that are not repetitive, non-standardized, unstructured, and not based on rules. Specifically, it studies the patterns in data, performs complex analysis, interacts with humans, and adapts to situation and time changes to generate insights for auditors.
Thus, auditors must give due consideration to the impact of technology on the audit process. They must understand the ever-growing relationship between automation and auditing, as it will change the audit scope and make it more efficient and more insightful. Rather, auditors must make the technologies more relevant for the audit process so that automation improves regulatory compliance, financial reporting process, management decision-making, operational efficiency, and revenue generation.
However, increased automation does not mean a diminishing role of auditors in the audit process. The advent of automated auditing calls for the development of new skills for auditors so that they can make their valuable contribution to the company using their professional skepticism, analytical judgment, and informed decision making. Conclusively, they must understand emerging technologies, open-mindedness, advanced analytical skills, resilience, and agility to make complex but valuable decisions for clients.