Use of Artificial Intelligence Techniques in the Implementation of Audit Tasks

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Table of Contents

Definition
Artificial Intelligence in Auditing
Risks of Using AI in Audit Sphere and Their Mitigation
The Role of AI in Audit Tasks
The Use of AI Techniques in Performing Specific Audit Tasks
Conclusion
References

Technologies alter the modern world and introduce radical changes to all spheres of human activity. The scientific progress and emergence of new devices triggered a reconsideration of old approaches and an establishment of new ones. They are characterized by the increased effectiveness, high output, and reduced input, which is vital for most organizations. For this reason, specialists look for new ways to integrate technologies and create a more digitalized environment with multiple options available for workers. Artificial Intelligence can be considered one of the most innovative solutions widely employed by different groups now.

It combines features attractive for organizations, such as effectiveness, reduced mistake rate, and stability. These factors are also vital for the auditing sphere as it contributes to the increased accuracy of conclusion and other benefits. The paper outlines the central aspects of using AI in the field and possible issues associated with its employment.

Definition

Artificial Intelligence is a complex and generalized term that is used to describe a unique technology. It presupposes building machines capable of simulating human thinking and intelligence to perform complex tasks unbailable and unresolvable by other devices (Rochwerger & Pang, 2021). In other words, it is a computer or software possessing some features of a human being with the ability to analyze the situation and make conclusions.

The availability of relevant and credible data is one of the basic demands for the stable functioning of the technology and the absence of critical mistakes (Moroney, 2020). Another vital characteristic of artificial intelligence is the ability to rationalize and offer strategies having the best chances of achieving the existing goal (Moroney, 2020). It helps companies to acquire a competitive advantage and avoid failures because of better planning and strategic analysis.

AI is closely linked with another concept that becomes fundamental today. Machine learning is a case of using AI to provide systems with a chance to automatically learn and evolve using the new experience generated during previous decision-making sessions (Shapiro, 2020). It does not presuppose additional programming or any other interferences, as AI with machine learning is capable of collecting and accessing data, its analysis, and structuring, which helps to improve future solutions and avoid mistakes. The benefits of machine learning include the increased autonomy, fast development, and applicability to various cases and environments (Shapiro, 2020).

Under these conditions, AI acquires the chance to carry on complex and human-like tasks, which offers a wide variety of options for its further implementation. Regarding the audit sphere, using AI is nowadays one of the trends leading to improved performance and better results.

In such a way, the fast spread of the technology and increased attention to it can be explained by the applicability and the opportunity to generate additional benefits. AI is viewed as the alternative to outdated methods that are not relevant today as it can learn and become more effective over time (AICPA, 2020). Furthermore, the growth of AI correlates with the constantly increasing availability of information and the rise of Big Data (AICPA, 2020). The term describes large and diverse sets of knowledge growing at ever-increasing rates (Moroney, 2020). The ability to analyze and process it is vital for the work of modern companies. For this reason, AI, with multiple opportunities for better data processing, is viewed as the potent measure to align the effective work of units and avoid failures or using irrelevant information.

Artificial Intelligence in Auditing

Implementation of AI in the audit sphere can offer multiple benefits. First of all, it is viewed as the central driver of efficiency and productivity in both internal and external audit functions (Ng & Alarcon, 2020). Machine learning tools provide specialists with an opportunity to analyze more contracts in a shorter period of time compared to traditional manual methods (Ng & Alarcon, 2020). It results in the growing effectiveness of a unit and contributes to the higher satisfaction of all parties involved in the process (Ng & Alarcon, 2020). Moreover, using unique pre-selected criteria, AI software can extract and analyze information from lease contracts, which is vital for the work of the sphere (AICPA, 2020). Automation of manual auditors’ tasks, such as documentation and correspondence, also results in better time management and growing effectiveness (Ng & Alarcon, 2020). These features make AI more attractive for the sphere of the audit.

Moreover, the implementation of AI can help to improve audit quality. Analyzing and comparing structured and unstructured data from numerous financial records, AI software can provide auditors with relevant knowledge about different contracts and their current state (Ng & Alarcon, 2020). Another advantage is the ability to make predictions and forecasts using historical transaction data and processing it to offer specific conclusions (Naqvi, 2020). The nature of AI and its features helps to increase the speed of accomplishing these tasks and ensures the higher accuracy of findings (Naqvi, 2020). For this reason, this innovative method is widely used by specialists.

Finally, benefits of using AI in the audit include the ability to identify anomalies and inform specialists about them. These might mean unusual payments, activities not detected by manual auditing, or unusual schemes (Naqvi, 2020). Detection of these features is vital for effective audit and credible conclusions. For this reason, this factor is also viewed as the central advantage of using and implementing AI in the audit sphere. The strong points mentioned above justify the importance of new technologies and help to understand why AI is viewed as a vital tool for the discussed field.

Risks of Using AI in Audit Sphere and Their Mitigation

Nevertheless, AI’s broad use in the audit sphere can also be associated with some risks that should be considered. First of all, employers might be resistant to using this technology to perform their tasks. They might have fears of being replaced by AI, which is one of the popular misbeliefs nowadays (Naqvi, 2020). Moreover, research shows that the unwillingness to use innovative technologies is linked to low computer literacy and awareness about the benefits of this method. Under these conditions, the lack of knowledge about AI can trigger specialists’ dissatisfaction and their desire to use traditional or manual approaches, which are less effective compared to the new ones offered by the AI.

Moreover, the risks of using AI in the audit sphere might involve some ethical issues. The technology enables the analysis of full population data (Dubber & Pasquale, 2021). It might give rise to some privacy issues and claims about the possibility of using AI and machine learning when working with people’s data (Naqvi, 2020). Furthermore, the correct functioning of the technology demands its improved understanding and the stable work of the IT department, which is a central requirement to the successful integration of this innovation in the life of different units. Otherwise, there is an increased risk of mistakes and failures preconditioned by poor alignment and integration (Dubber & Pasquale, 2021). Under these conditions, these risks are vital for considering the establishment of the framework for the increased effectiveness.

The mitigation of these risks is vital for the alignment of the successful work of the unit and the creation of the basis for future evolution. The first strategy is the provision of additional training for specialists to ensure they have an enhanced vision of how the AI can be used in various settings and applied to different environments to ensure better outcomes (Naqvi, 2020). Training will all guarantee that workers will not have resistance towards the use of technologies and possess the right idea of how machine learning and AI should be integrated into the work of their unit. This method will contribute to the boost of performance due to the increased motivation and awareness among the staff.

The Role of AI in Audit Tasks

Planning for Audit Task

Any effective audit process always starts with a well-thought-out audit plan which is vital for attaining success. An auditor is responsible for organizing his/her activities, varying between clients, and establishing risk assessment procedures to ensure positive results (Naqvi, 2020). However, one of the leading audit problems is that plans might have gaps influencing the process and decreasing the chance for success (Naqvi, 2020). The degree of uncertainty and unidentified risks are particular to a certain field and should be viewed as an internal part of the audit process (Naqvi, 2020). Under these conditions, any auditor faces several challenges:

The need to reduce unknown risks.
To consider all blind spots.
The necessity to confirm initial assessments about the industry (AICPA, 2020).

Today, these goals can be accomplished by using unique AI technologies that are available to auditors.

For instance, using specific machine learning and AI applications, auditors can improve planning for audit tasks. The team can enhance the risk assessment, which is a vital part of planning, by using AI tools, allowing to examine all company’s transaction data and acquire insights (Deloitte, 2018). Furthermore, because of the availability of big data portions and the ability to process it by using the most effective risk assessment techniques, AI helps to identify threat areas that can be previously omitted (Naqvi, 2020). It will lead to improved planning and minimization of the chance for a new audit aimed at correcting mistakes (Deloitte, 2018). Under these conditions, the use of machine learning and AI software becomes an integral part of the modern audit sphere as it helps to develop an effective audit plan that can be used by specialists to acquire desired outcomes and avoid critical mistakes.

Execution for Audit Task

Innovations also accelerate execution activities meaning that the audit sphere benefits from the emergence of new opportunities. For instance, this stage presupposes execution of actual audit work, evaluation of the process and control design, testing methods to outline control effectiveness, and examination of performance. Furthermore, there is a need for discussing items formulated as findings and their incorporation into a single summary and report. The scope of this task means that a specialist might face some problems at any of these stages because of the limited opportunity to consider all data or inability to look at the analyzed case from another angle, considering the existence of other sources vital for the scenario.

Application of the AI tools supplier with machine learning capabilities can lead to better performance in the given sphere. Thus, Ng and Alarcon (2020) admit that today auditors have the chance to avoid extra pressure during the execution phase due to the use of computers and unique software. For instance, the emergence of audit assistants contributes to better planning and execution activities via the high level of automation and data analysis (Ng & Alarcon, 2020). A specialist can use a set of different techniques, such as reporting and data structuring, by employing the AI tools available for them. It will improve outcomes and ensure the lack of gaps or failures.

Moreover, Ng and Alarcon (2020) state that by applying the innovative tools to the execution phase, specialists acquire the chance to reduce time, input, and maximize output due to the availability of data and new analysis methods. Under these conditions, using AI is recommended for the execution of audit tasks as it offers multiple benefits and helps to minimize mistakes or failures in the process.

Reporting for Audit Task

As stated previously, reporting is another vital part of the audit process as it presupposes structuring data, its synthesis, and discussion by specialists. The traditional report includes the auditor’s opinion on whether a unit’s financial statements comply with the existing regulations and offer data sets to prove this assumption (Ng & Alarcon, 2020). The document is critical as banks, creditors, or other agencies can require an audit of financial statements to prove the current state of a person or a firm (Naqvi, 2020). Under these conditions, the phase becomes vital for the whole process as it summarizes the structures conclusions.

The existing research body proves the idea that AI can be applied to reporting to attain better results. For instance, Naqvi (2020) says that using machine learning and specific software, it is possible to make reports more comprehensive and understandable for other individuals. At the same time, AI can structure the presented data and organize it into specific portions leading to a better vision of a complete image and every single scenario at the same time. Ng and Alarcon (2020) also state that AI can offer special patterns that can be used by an auditor to perform the planned task, process data, and organize it. Under these conditions, reporting using the AI becomes simpler and demands less time.

Altogether, works show that the discussed technology has multiple applications in the modern audit sphere (Naqvi, 2020; Ng & Alarcon, 2020). It helps to minimize risks by their better assessment, investigation, and discussion. Furthermore, most researchers state that the use of AI leads to more effective audit as it helps to improve areas that are traditionally weak or demand additional investigation or intervention.

The Use of AI Techniques in Performing Specific Audit Tasks

Fraud Investigation

Fraud investigation and detection play a central role in modern audit processes. It presupposes the ability to find activities or inappropriate financial data, investigate them, and use specific measures to manage them (Ng & Alarcon, 2020). One of the possible techniques to assist in performing this task is the use of Intelligent Analytics and AI. Combining the supervised learning algorithms and most relevant methods for fraud detection, this innovative tool helps to acquire an enhanced understanding of companies’ behaviors. It leads to the better tracing of the slightest changes and detection of frauds, which is vital for audit (Ng & Alarcon, 2020). AI also helps to free up fraud analysts, meaning they can perform other activities.

In such a way, researchers agree that fraud investigation benefits from the rise of the given technology. Naqvi (2020) says that AI helps to complete data analysis within milliseconds, which is vital for detecting and analyzing complex patterns and concluding about their nature. Additionally, AI can help to eliminate tasks demanding much time and provide fraud analysts with a chance to focus on critical cases requiring their interference (Naqvi, 2020). Under these conditions, fraud investigation benefits from the development of AI as it offers new ways to work with data and process it.

Risk Assessment

AI is also one of the key drivers transforming risk assessment strategies. Machine learning models can analyze large amounts of data in a reduced period of time to acquire results vital for making decisions and selecting the correct approach to work with risks and mitigate them. For auditors, the employment of AI is a key to better planning as they acquire the chance to use results offered by AI software and use them in making plans and executing the major processes. Effective risk assessment demands the high accuracy of data and its analysis, which is achieved by using innovative solutions.

The current body of literature also emphasized multiple benefits associated with the use of AI regarding risk-assessment procedures. Naqvi (2020) states that the availability of numerous machine learning models provides auditors with a chance to perform a better risk assessment and conclude about the exiting risks avoiding gaps in knowledge or failures. AI and risk management align when the necessity for handing and processing unstructured data emerges (Bullock, 2019). Under these conditions, most AI techniques can be applied to the audit sphere to outline existing risks by reviewing available data and structuring it.

Documents Review

Documents review also benefit from the emergence of new technologies. Ng and Alarcon (2020) outline an AI-powered document review technology allowing to process entire sets of documents. These might include invoices, expenses, texts, data collections, and other descriptions (Ng & Alarcon, 2020). Applying specific software to such cases, an auditor acquires a chance to reduce the time needed to work with this information, and, moreover, he/she can benefit from the increased effectiveness guaranteed by using AI. Furthermore, about 72% of financial controlling and documentation can be automatized using AI document review technology (Ng & Alarcon, 2020). It means a significant increase in the effectiveness of investigations and the ability to free specialists to work on other critical tasks.

At the same time, AI ensures the better accuracy of documents’ processing and analysis. Following the result of the study by Sadangi et al. (2020), auditors make more mistakes when reviewing specific documents compared to the machine learning models. Under these conditions, it is possible to conclude that document review can be improved by applying AI tools focused on better data analysis and processing. Most researchers support this idea and emphasize the need for the further development of this method to support the evolution of the sector.

Internal Control Evaluation

Evaluation of internal control is another activity vital for the audit sphere. It presupposes an examination of how the organization manages its internal control systems and deals with various operations (Huang & Rust, 2018). A potent internal control system helps to reduce the risk of frauds, failures, or inappropriate financial data (Huang & Rust, 2018). The scope of the activity leads to the emergence of new methods to improve outcomes, and using AI is one of the possible options. Sadangi et al. (2020) say that integrating this method allows auditors to align better internal control systems as they are provided with relevant and topical data about the work of organizations and factors influencing their internal processes.

Furthermore, using innovative technologies, specialists acquire the chance to create the basis for future improvement. Winslow (2017) is sure that internal control evaluation with the help of AI will provide this technology with new experiences vital for processing data, and it increased accuracy during new audit sessions. For this reason, by applying machine learning tools to different cases, auditors promote better learning and can expect benefits. In such a way, using innovative technologies in the sphere of audit, individuals can reconsider old approaches and replace them with new ones, more effective today.

Conclusion

Altogether, the existing body of literature shows that AI is a critically important technology that can be used in the audit sphere to increase its effectiveness. By employing machine learning models, a specialist can improve their planning, executing, and reporting activities. Moreover, most authors view AI as the future of the audit sphere as it offers multiple techniques aimed at simplifying tasks and promoting better outcomes. The further digitalization of society creates the basis for the wider use of AI and machine learning applications, meaning that the sphere of the audit will continue to alter and acquire new features.

References

AICPA. (2020). Audit and accounting guide: Not-for-profit entities. Wiley.

Bullock, J. B. (2019). Artificial intelligence, discretion, and bureaucracy. The American Review of Public Administration, 49(7), 751–761. Web.

Deloitte. (2018). Adopting automation in internal audit. Web.

Dubber, M., & Pasquale, F. (2021). Oxford handbook of ethics of AI. Oxford University Press.

Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. Web.

Moroney, L. (2020). AI and machine learning for coders: A programmer’s guide to artificial intelligence. OReilly Media.

Naqvi, A. (2020). Artificial intelligence for audit, forensic accounting, and valuation: A strategic perspective. Wiley.

Ng, C., & Alarcon, J. (2020). Artificial intelligence in accounting: Practical applications. Routledge.

Rochwerger, A., & Pang, W. (2021). Real world AI: A practical guide for responsible machine learning. Lioncrest Publishing.

Sadangi, C. K., Mohapatra, S., & Sinha, K. (2020). Automation of local fund audit (ALFA): Aligning the goal of audit in nation. FIIB Business Review, 9(2), 94–101. Web.

Shapiro, D. (2020). Artificial Intelligence for Internal Audit and Risk Management. Towards Data Science. Web.

Winslow, E. (2017). Statistical audit automation: Applying artificial intelligence techniques. Auditmetrics.

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