Order from us for quality, customized work in due time of your choice.
Introduction
The emergence of big data cultivated radical shifts in audit and data processing. Specialists acquired new opportunities for working with clients and offering them a more detailed and structured analysis (Li et al., 2016). At the same time, a set of new challenges emerged as there is a growing need for using more complex business analytics that will help to support the positive changes (Bouaoula et al., 2019). For this reason, the question of what techniques and methods of data processing and analysis are most effective becomes promising and should be given much attention (Manko, 2021). Furthermore, the topic is becoming more relevant because of the rise of technologies and new methods to deal with numbers and structure big data. Traditional methods are replaced with new ones, relying on Artificial Intelligence (AI) and data analysis and prediction models. Under these conditions, the challenge of using different approaches becomes more significant, and it is vital to acquire an enhanced vision of how various techniques can be used to attain desired outcomes.
Background
The existing literature devoted to the given topic also emphasizes the increased relevance of Big Data and the constant emergence and improvement of new methods integrated into the public audit to ensure the correct understanding of the situation. Numerous authors state that business analytics and machine learning are significant spheres of knowledge that are on the rise today and are widely used by various analysts to process data sets and create forecasts or relevant models (Gandomi et al., 2022). They offer a wide variety of tools that can be employed to attain the desired outcomes. At the same time, business analytics and machine learning rely on various mechanisms and views on how information should be managed and investigated (Kanchanapoom & Chongwatpol, 2021). For this reason, multiple research works focus on investigating these fields and determining the differences and similarities between them to acquire a better vision of these two approaches and conclude about their applicability.
Thus, the modern public or business audit implies evaluating a certain organization and whether its financial and other data is correct, meets the existing standards, and can be used to plan further development. It means that analytical procedures (APs) become especially important as they are viewed as integral parts of the audit process. Appelbaum et al. (2018) determine it as the process consisting of numerous evaluations of financial information performed by studying plausible relationships between financial and non-financial data. In such a way, the given definition means that specialists working in the sphere of audit and performing APs should focus on analyzing various variables simultaneously to determine the nature of correlations between them and ensure the forecast and plans rely on relevant data with a low probability of mistake (Appelbaum et al., 2018). For this reason, the most effective and practical tools should be used to acquire the desired outcome.
The existing body of literature offers many research papers devoted to investigating APs and how they are performed. Thus, machine learning and business analytics are considered two critical aspects of the audit and data investigation process (Dickey et al., 2019). Most authors are sure that AI and machine learning are the most innovative methods of working with data and increasing the accuracy of calculations (Dickey et al., 2019; Manko, 2021). At the same time, business analytics also uses multiple creative and digital techniques to acquire the planned outcome and guarantee that analysts have all tools necessary for effective planning and forecasting. Under these conditions, it becomes critical to compare the methods to determine the similarities and differences and conclude about the possible applications and perspectives. Fort this reason, the presented literature review summarizes and organizes the knowledge about these two methods and provides the basis for their comparison. It enhances the improved understanding of business analytics and machine learning techniques and contributes to implementing these methods in real-life conditions.
Machine Learning
Comparing the two selected means, it is vital to introduce their clear definitions and peculiarities of their functioning regarding the selected sphere. Thus, one of the broad definitions of the term views it as a specific model of artificial intelligence used when working with data and helping to automate analytical building (Vijayakumar, 2021). In such a way, machine learning is linked to AI, or a computer’s ability to do tasks that traditionally require human involvement and intelligence (Dickey et al., 2019). The definition shows that technologies become the basis of this model, leading to the minimized need for people’s inference (Dickey et al., 2019). Thus, machine learning, as a form of AI, is characterized by the ability to use specific models to perform data analysis to realize specific patterns observed during a particular period of time (Hoogduin, 2019). Hoogduin (2019) says that one of the major machine learning’s advantages is that it uses a broad set of previous data to work with the current one. For this reason, the increased accuracy of assumptions conclusions is attained.
Another critical factor explaining the growing popularity of machine learning is the iterative approach. It implies the adaption of the acquired information and model in the course of research and data processing (Hoogduin, 2019). In such a way, machines are programmed to improve their work regarding the analyzed data and the tasks they perform. Dickey et al. (2019) say that it allows machine learning analytic tools to become highly automated and continuous, vital for the modern accounting sphere regarding the constantly growing amount of information and data that should be processed. Under these conditions, machine learning is similar to traditional statistics as it focuses on working with regularities and correlations between various numbers. However, Hoogduin (2019) admits that it differs from the conventional approach in execution or how specific goals are achieved.
Thus, traditional statistical analysis mainly relies on probability theory and distributions. An auditor or analyst works with data sets to compare variables and acquire information about them (Appelbaum et al., 2017). However, machine learning uses another method to discover a unique combination of mathematical equations helping to predict outcomes and make a reliable forecast (Manko, 2021). In such a way, it becomes an applicable tool to perform cluster analysis, linear regression, or other functions critical for analytical procedures and audit results. In such a way, the modern literature views it as a potent and promising tool to assist specialists working with Big Data.
Business Analytics
Business analytics (BA) is another broad field of knowledge related to audit and data processing. Dickey et al. (2019) define it as a continuous process of transforming data into business-related insights to guarantee an improved understanding of the current situation and make informed decisions. Regarding the growing complexity of the business world and the increased amounts of data that should be processed it becomes critical to introduce measures that help to predict the further evolution of the business and create the basis for enhanced decision-making (Dickey et al., 2019). For this reason, most researchers admit that business analytics is a highly innovative field of knowledge with multiple applications and approaches used to process new information and structure it (Dickey et al., 2019). At the same time, it remains less dependent and reliant on AI and machine learning, meaning that the human factor plays a critical role (Manko, 2021). For this reason, the choice of an effective method of data processing acquires the top priority.
Because of the high pace of digitalization, business analytics is closely linked to business intelligence. Kanchanapoom and Chongwatpol (2021) view it as a field of knowledge at the edge of computer science, statistic analysis, and data processing. Thus, business intelligence (BI) encompasses business analytics, data visualization, infrastructure, tools, and data mining to help organizations to make informed decisions and avoid critical mistakes (Bouaoula et al., 2019). Under these conditions, BI becomes a critically important framework that can be employed by analysts to process all financial data linked to a certain phenomenon and guarantee that an organization will continue to evolve (Bouaoula et al., 2019). Traditional business intelligence is characterized by the reduced dependence on AI, while modern BI relies on computer methods and is characterized by the massive use of various programs designed to assist analysts (Bouaoula et al., 2019). For this reason, business analytics becomes closely associated with BI and can be compared to machine learning because of the differences in methods and approaches to perform particular functions.
Comparison Criteria
As stated previously, the modern literature devotes much attention to comparing machine learning and BA to attain an improved understanding of their peculiarities. For this reason, numerous comparison criteria are suggested. For instance, Appelbaum et al. (2018) say that from the business analytics perspective, the traditional APs process can be considered a component of External Audit Analytics (EAA) (94). It pertains to all BA techniques and encompasses a set of knowledge and approaches used to analyze particular data sets (Appelbaum et al., 2018). For this reason, specific dimensions, such as domain, orientation, and technique, are vital for analyzing both these fields. Moreover, these categories can be applied to machine learning as the process also focuses on analyzing data and forecasting (Appelbaum et al., 2018). At the same time, Hoogduin (2018) says that the primary functions and models should also be viewed as the point of difference vital for the comparison. Under these conditions, the following criteria are used to analyze the literature linked to BA, BI, and EAA as its component and machine learning and compare the main methods and tools.
Functions
The body of work can be viewed as the first area for comparison. Machine learning and BA, or BI, are employed to attain similar goals, but their functioning differs. Thus, BI is used to process a selected business in the desired path (Appelbaum et al., 2018). Using EEA approaches and tools, a specialist monitors external and internal environments by collecting, retrieving, and disseminating big volumes of data and information (Appelbaum et al., 2018). From this perspective, BA can be viewed as the process of constant collection and generation of knowledge vital for the future evolution of an organization (Appelbaum et al., 2018). However, Appelbaum et al. (2018) emphasize that BA contributes to generating the final knowledge, or information that is used by managers to make informed decisions and conclude about the current state of the company. The given results can also be used as a part of a bigger report; however, they cannot be used to generate additional knowledge on how to process similar cases in the future.
Machine learning is also a potent tool to work with Big Data and structure it. Vijayakumar (2021 considers this approach a future of audit and analytical procedures. However, it is critical to admit the difference between BA and BI. As against traditional audit methods, or methods supported by computers, machine learning’s central function is to evolve and improve decision-making using the results of previous investigations and conclusions (Vijayakumar, 2021). In such a way, the central goal of data processing is not limited to creating the basis for making solutions. Instead, machine learning allows one to learn from past experiences and acquire new functions. Ilmudeen (2021) assumes it as one of the central advantages of the given framework as, over time, it becomes able to resolve more complex tasks and demonstrate better performance. For this reason, it makes machine learning an evolving phenomenon vital for the sphere in the future.
Approaches to Working with Data
The existing literature also outlines the differences in approaches to working with data peculiar to BA and machine learning. Thus, BI and EEA view available information as a set of facts that should be processed to present the desired conclusion (Appelbaum et al., 2018). Kanchanapoom and Chongwatpol (2021) say that BI is focused on converting raw data into information that can be used to improve decision-making and ensure analysts acquire an enhanced vision of the company’s work and problems it might have. In such a way, existing data visualization and processing techniques are focused on organizing and structuring existing data sets and presenting them in a manner understandable to a broad audience. BI provides analysts with tools to simplify their tasks and acquire needed results. These include statistical analysis and computer applications that are broadly used nowadays (Kanchanapoom & Chongwatpol, 2021). Using these methods, a specialist transforms raw data into an understandable report that helps to understand the company’s work.
Thus, machine learning techniques are different and focus on attaining other goals. Kanchanapoom and Chongwatpol (2021) say that although machine learning also processes data to structure and present information, it also deploys specific data mining techniques to create new models that can be used for forecasts in the future. It is one of the major factors differentiating machine learning’s approach to working with data and processing it. As stated previously, machine learning is a form of AI evolving regarding data it has already analyzed. For this reason, the increased amount of information leads to generating new models and algorithms to predict new alterations and correlations between various aspects (Bouaoula et al., 2019). It also results in better decision-making attained due to the ability to rest on previous information to forecast changes in regularities and relations (Kanchanapoom & Chongwatpol, 2021). Under these conditions, machine learning differs from BI and BA as it works with raw data sets to structure them and develop new models that can be used in the future.
Domain
As previously outlined, the domain is a critical criterion for comparing various practices and methods of the audit. Thus, Appelbaum et al. (2018) say that the domain of EAA is linked to the stages of the audit cycle where BA and BI methods can be used. In this regard, the BA’s domain implies consideration of differences between internal and external audit analytics in terms of a specific context, determination of overlap between various variables, and investigation of analytic data needs (Appelbaum et al., 2018). The BA domain is mainly focused on analyzing data linked to the audit and providing structured reports and forecasts about the current organization’s state. At the same time, working with Big Data, AI can lead to predicting specific trends necessary for stable development (Li et al., 2016). In such a way, using clustering models and predictive statistics, specialists can increase the effectiveness of their investigation and focus on the outlined domains. It will contribute to enhanced data processing and results.
From another perspective, the machine learning domain differs from BA’s one. Ilmudeen (2021) says that it focuses on data science and AI, which influences how it works and acquires outcomes. The technology remains fundamentally dependent on models. It means that the spheres of application differ regarding the model and algorithm used by the technology to work with a particular data set. In terms of the business environment and audit, machine learning also focuses on determining relations between variables and discovering specific relations between investigated phenomena (Ilmudeen, 2021). However, as against traditional statistics and BI, machine learning also works with domains involving technology and forecasting (Ilmudeen, 2021). For this reason, it leads to structuring data in specific ways and offering conclusions that can be used in forecasting activities. In such a way, BA and machine learning have different domains, which can be explained by the peculiarities of the approaches and their nature.
Orientation
Orientation is another factor that can be discussed to compare machine learning and BA. Appelbaum et al. (2018) say that EAA includes descriptive, predictive, and prescriptive orientations. Regarding the interrelation of EAA, BI, and BA, the given division is relevant for the discussion. Thus, descriptive orientation is focused on questions about what happened and usually implies using descriptive statistics, KPIs, and visualization (Appelbaum et al., 2018). Most academic resources are focused on analyzing this type of BA orientation because of its scope and spread. Predictive EAA is viewed as another step toward better data analysis and predicting what could happen regarding the existing numbers (Appelbaum et al., 2018). It broadly uses predictive and probability models, forecasts, and statistical tools and models (Appelbaum et al., 2018). Finally, prescriptive EAA focuses on offering specific solutions or the most probable outcome of a specific solution (Appelbaum et al., 2018). The forecasting rests on the two components such as actionable big and varied data and a validation system (Appelbaum et al., 2018). In such a way, these three orientations are vital for applying BA in real-life settings.
Machine learning’s orientation can be described in a different way. Thus, Dickey et al. (2019) say that it is difficult to introduce the correct description of this aspect because of the numerous tasks performed by the given approach. Descriptive, predictive, and prescriptive orientations peculiar to BA can also be found in machine learning frameworks (Dickey et al., 2019). It is used to process big portions of data, analyze them, and make specific forecasts linked to the work of the organization and its further evolution. However, Hoogduin (2019) admits that machine learning is more oriented toward creating algorithms and models that can be used in the future. It makes it oriented on making not only relevant conclusions but establishing the basis for new operations and their enhanced effectiveness. For this reason, machine learning is characterized by the shifted orientation toward synthesizing data, making it a part of a new cycle.
Techniques
The scope of techniques used in BA and machine learning also differs. Speaking about EAA, as a part of BA, Appelbaum et al. (2018) say that regarding the availability of big data, this form of analysis may result in a prospective analysis approach. It involves techniques outlining alternative actions available to an auditor (Appelbaum et al., 2018). For this reason, EAA and BI techniques might imply using various and diversified sources of big data to form a specific audit option. More complex EAA techniques demand reliable evidence and actions aimed at quantification and arriving at prescriptive analytics (Appelbaum et al., 2018). BA relies on traditional audit techniques supported by statistical analysis and working with available data sets. It contributes to transforming existing raw information into clear statements necessary for understanding the current situation and making informed decisions (Appelbaum et al., 2018). These techniques are a vital part of BA today and help to make it an effective audit tool used by numerous specialists with different purposes.
Machine learning techniques have other peculiarities differentiating them from the approaches used in BA and EAA. Thus, Vijayakumar (2021) says that automated approaches are widely used in various domains to ensure the high effectiveness of computerized monitoring and regulation. It leads to error-free observation and control without compromising accuracy (Dickey et al., 2019). In such a way, machine learning techniques presuppose using specially designed algorithms critical for introducing tasks and ensuring positive results are acquired. These include regression, classification, building decision trees, clustering, and neural networks (Dickey et al., 2019). Using these tools, machine learning applications demonstrate increased accuracy due to the effectiveness of used algorithms and models built regarding previous data. In such a way, BA and machine learning techniques differ because of the adherence to specific data processing techniques. Statistical analysis peculiar to EAA methods is replaced by models used to increase the accuracy and speed of data processing.
Use of Algorithms
The existing body of research emphasizes the fact that BA uses specific statistical methods to acquire needed results. Kanchanapoom and Chongwatpol (2021) say that business intelligence approaches do not depend on algorithms and are highly reliant on the skills of an auditor and his/her experience in working with data. Although there are numerous applications and computational tools available to such specialists, the human factor plays a critical role as all decisions are made by an individual. No algorithms are used; instead, an auditor selects techniques most appropriate for a specific scenario. The sequences of actions selected to perform an analysis of a particular raw data set are selected by a person responsible for the outcome (Kanchanapoom & Chongwatpol, 2021). They are not automated, meaning that the approach might differ regarding the conditions or the preferences of an auditor (Kanchanapoom & Chongwatpol, 2021). For this reason, BA is characterized by the reduced influence of models and algorithms.
Machine learning techniques, on the contrary, rely on using algorithms and models. Dickey et al. (2019) view them as the critical aspect of the given approach, explaining its strong and weak sides. Data analysis and interpretation provide machine learning applications with the basis for creating new models that can be viewed in the future. Furthermore, similar to more experienced auditors, machine learning applications with numerous diversified algorithms become more effective in resolving new tasks and data investigation (Gandomi et al., 2019). It is attained due to the ability to learn, which is central to technology. Having resolved numerous cases, the machine acquires tools for faster consideration in the future and the provision of more reliable and credible decisions regarding strategic planning (Dickey et al., 2019). Under these conditions, the use of models and algorithms is the most important distinctive feature of machine learning. It helps to improve its work continuously and adapt to specific conditions peculiar to the work of a particular unit.
Advantages
The compared approaches have their advantages and disadvantages, justifying their use in various situations and applicability to some cases. For instance, Appelbaum et al. (2017) say that by applying the methods and tools of BA and BI, auditors acquire the chance to increase the speed of analysis and guarantee increased organizational efficiency. BA also establishes the basis for data-driven decisions which rely on conclusions made by a specialist (Appelbaum et al., 2017). Using computer tools and specific applications, a specialist can work with big data and analyze it regarding the existing goals and purposes. At the same time, the human factor means that it is possible to introduce the necessary alterations as fast as possible. It ensures an immediate response to altering conditions and leads to better results.
Machine learning techniques also have several critical advantages emphasized by numerous authors. First, the use of AI and computers means the minimal involvement of human beings in the calculation and data analysis process (Vijayakumar, 2021). For this reason, the risk of mistakes reduces significantly, while the accuracy of conclusions becomes much higher (Ilmudeen, 2021). It makes machine learning one of the most advantageous tools for working with information. Moreover, Vijayakumar (2021) states that theoretically, machine learning applications can work with unlimited amounts of data. It means that regarding the growing sophistication of the business world, machine learning is one of the most promising ways to deal with big data and ensure it is used for decision making.
Disadvantages
However, the existing body of research admits several disadvantages peculiar to the compared frameworks. For instance, Ilmudeen (2021) assumes that BA methods can be insufficient in terms of the big and international corporations because of the too significant amounts of data that should be processed. An auditor might face the complex task of transforming raw data into an understandable report that can be presented to the audience (Ilmudeen, 2021). For this reason, BI and similar analytical tools might have limited effectiveness in cases characterized by the increased complexity and the presence of multiple variables that should be considered. The analysis of different data sources can also be complicated because of the limited capabilities of a certain application or analyst (Manko, 2021). These disadvantages should be considered when using BA for various purposes.
Machine learning also has specific disadvantages influencing its use in the audit. Thus, Gandomi et al. (2022) admit that machine learning requires massive data sets that train and create new algorithms. It can be a challenge to employing machine learning in small enterprises (Dickey et al., 2019). At the same time, it requires specific resources and time to align the work appropriately and guarantee that demanded data will be processed (Dickey et al., 2019). Finally, machine learning techniques demand specialists who possess the needed skills to use these tools and guarantee they will contribute to improving the work of an organization and decision-making procedures. In such a way, the given approach has its limitations influencing its use in various cases.
Conclusion
Altogether, business analytics and machine learning are the critical techniques used nowadays in audit and analytics. The relevance and critical importance of these measures are evidenced by numerous research papers devoted to them. The researchers state that numerous factors should be considered regarding these frameworks. Thus, the comparison shows that BA relies on available statistical analysis tools selected by an auditor, while machine learning focuses on creating and using models and algorithms. These differences come from the nature of the discussed approaches and their orientation. However, it is possible to attain enhanced decision-making and strategic planning due to the use of BA and machine learning tools. The comparison also shows the differences in domains and orientations, which should be considered when discussing these two frameworks.
References
Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2017). Big data and analytics in the modern audit engagement: Research needs. A Journal of Practice & Theory, 36(4), 1–27. Web.
Appelbaum, D.A., Kogan, A. & Vasarhelyi, M.A. (2018). Analytical procedures in external auditing: A comprehensive literature survey and framework for external audit analytics. Journal of Accounting Literature, 40, 83–101. Web.
Bouaoula, W., Belgoum, F., Shaikh, A., Taleb-Berrouane, M., & Bazan, C. (2019). The impact of business intelligence through knowledge management. Business Information Review, 36(3), 130–140. Web.
Dickey, G., Blanke, S., & Seaton, L. (2019). Machine learning in auditing: Current and future applications. The CPA Journal. Web.
Gandomi, A., Chen, F., & Abualigah, L. (2022). Machine learning technologies for Big Data analytics. Electronics, 11(421), 1-4. Web.
Ilmudeen, A. (2021). Big data analytics capability and organizational performance measures: The mediating role of business intelligence infrastructure. Business Information Review, 38(4), 183–192. Web.
Hoogduin, L. (2019). Using machine learning in a financial statement audit. Compact, 4. Web.
Kanchanapoom, K., & Chongwatpol, J. (2021). Applications of business intelligence and marketing analytics in the complementary and alternative medicine industry. Journal of Information Technology Teaching Cases, 11(1), 30–42. Web.
Li, H., Dai, J., Gershberg, T., Vasarhelyi, M. (2016). Understanding usage and value of audit analytics for internal auditors: An organizational approach. International Journal of Accounting Information Systems, 28, 59-76. Web.
Manko, B. A. (2021). Big data: The effect of analytics on marketing and business. Journal of Information Technology Teaching Cases. Web.
Vijayakumar, K. (2021). Computational intelligence, machine learning techniques, and IOT. Concurrent Engineering, 29(1), 3–5. Web.
Order from us for quality, customized work in due time of your choice.