Content area
The use of artificial intelligence (Al) builds up the accounting system efficiency, increases data entry accuracy and simplifying the accounting process. The aim of the study is to prove the effectiveness of modern Al-based information technologies (IT) in accounting and the possibilities of Al application for process optimization. The effectiveness and efficiency were proven using comparison methods, statistical analysis, graphical cause-and-effect analysis, modelling using the linear regression method. The assessment was carried out using quantitative and qualitative indicators of labour productivity and process optimization. The results of the study showed that 18 accounting department employees on average are needed to perform standard transactions in the companies studied without Al. With Al, 1 person can handle such a volume of work. Accordingly, with the implementation of Al, the average reduction in Transaction Processing Time per Week is 696.26 hours. Regression analysis confirmed that the implementation of Al increases the companies' productivity in terms of Transaction Processing Time. Reducing the Data Processing Complexity by one unit leads to a reduction in transaction processing time by 592.69 seconds. Each percent increase in Data Entry Accuracy contributes to a reduction in processing time by 5135.51 seconds. The prospects for implementing Al in accounting include further improving algorithms to increase the accuracy and speed of transaction processing, optimizing material and time consumed.
Abstract: The use of artificial intelligence (Al) builds up the accounting system efficiency, increases data entry accuracy and simplifying the accounting process. The aim of the study is to prove the effectiveness of modern Al-based information technologies (IT) in accounting and the possibilities of Al application for process optimization. The effectiveness and efficiency were proven using comparison methods, statistical analysis, graphical cause-and-effect analysis, modelling using the linear regression method. The assessment was carried out using quantitative and qualitative indicators of labour productivity and process optimization. The results of the study showed that 18 accounting department employees on average are needed to perform standard transactions in the companies studied without Al. With Al, 1 person can handle such a volume of work. Accordingly, with the implementation of Al, the average reduction in Transaction Processing Time per Week is 696.26 hours. Regression analysis confirmed that the implementation of Al increases the companies' productivity in terms of Transaction Processing Time. Reducing the Data Processing Complexity by one unit leads to a reduction in transaction processing time by 592.69 seconds. Each percent increase in Data Entry Accuracy contributes to a reduction in processing time by 5135.51 seconds. The prospects for implementing Al in accounting include further improving algorithms to increase the accuracy and speed of transaction processing, optimizing material and time consumed.
Keywords: artificial intelligence; accounting; financial management; digital environment; automation; machine learning; data analytics.
JEL Classification: M11; M15; M21; M41; C01.
Introduction
Solving complex accounting issues in the system of economic information flow and the formation of business on an increasing scale of accounting becomes relevant and appropriate. The solution implies the adoption and use of Al and programmes based on it. Al enables arranging information, provide quick access to databases, analyse and structure information, perform complex calculations in a short period of time, eliminate human error, etc. This is not a complete list of advantages, but it justifies the need to move accounting to a new level (Brukhanskyi, Spilnyk, 2020).
The latest implementation was Al, blockchain technologies, large databases. These technologies and programmes based on them have reduced the monotony and uniformity of work to almost zero, increasing the potential for data processing. Therefore, the use of Al in business management has become a relevant topic. Questions about developing strategies and methods for using Al form the basis of many studies by researchers and prominent figures (Gupta et al. 2021).
At the same time, these innovations do not leave aside the economic aspects of life and development. So, the integration of the achievements of the scientific and technical process into the life of enterprises and farms is reflected in accounting, which is a constant tool of economic information (Benko, Moskaliuk, 2022). The combination of Al, the achievements of scientific progress and human economic activity, which leads to the growth and prosperity of the economy, leads to increased forms of control and accounting. This leads to the search for new safe and effective methods of solving problems, one of which is the implementation of Al. Al accumulates, groups and systematizes information, and is used for business management (Butynets et al. 2022; Zaporozhets, 2020).
Globalization of business management processes consists in using the latest digital technologies that improve the quality of management processes, speed up accounting, increase the effectiveness of activities, and enhance security. However, the globalization process is accompanied by a number of problems that arise in any innovation processes (Smiesova et al. 2019). This is especially true for accounting, where these implementations must be organized from the perspective of law, regulation, and reliability. Moreover, the process must be accompanied by maintaining confidentiality for each enterprise (Al-Okaily & Alsmadi, 2024). Therefore, the issues of creating uniform legal norms for the digital process and developing applied tasks for the implementation of ITs of a specific type are relevant.
An important feature of the study is that it fills the existing gaps in the scientific literature on the implementation of Al in accounting activities, offering a quantitative assessment of its impact on the performance of accounting systems. The novelty of the work is the proposed approach to modeling the speed of transaction processing after the implementation of Al and identifying key factors that affect the efficiency of automated processes.
The aim of the research is to reveal the potential of modern Al-based ITs in accounting and the possibilities of its application for process optimization. Research objectives:
- Analyse the work of the enterprise for the year using traditional accounting methods (manual accounting, use of Excel tables, and classic accounting programmes) and Al-based programmes;
- Conduct a comparative analysis of the obtained data by qualitative and quantitative performance indicators (number of errors, processing time of transactions, processing complexity, etc.);
- Describe the relationship between the studied indicators using a causal cyclic diagram;
- Conduct a regression analysis of the impact of indicators on productivity, expressed through the indicator of transaction processing time.
1. Literature Review
Globalization opens up great opportunities for business and entrepreneurship, providing a number of positive tools in their work. Economic aspects are aimed at facilitating business and accounting, and therefore their implementation should be based on research data confirmed by time. Prominent researchers have shown a general assessment of Al in the areas of accounting (Nikonenko её al. 2022, Wang et al. 2022). In contrast, the work (Savkiv, Kuzmin, 2023) reflects the prospects for introducing Al into accounting when transitioning to a new level of reporting. The works (Hasan, 2021; Thapa & Camtepe, 2021) reflect a number of shortcomings and unresolved issues, such as protecting confidential data, conducting training, and the difficulties of transitioning to a new level of programmes and tools that appear today. The study (Sunardi et al. 2020) reproduces the problems of fraud and lack of transparency in reporting. The article (Cho, 2024; Lysenko et al. 2024) examines the issues of harmonization of international standards and cooperation at the level of global cybersecurity. The researchers' results are valuable for outlining the key problems of accounting in the context of progress and globalization. However, the authors' approaches lack specifics, as they define the Al implementation as a universal way to solve problems without providing specific evidence.
More practical results were obtained in works describing the Al integration into the accounting of companies. Such issues of implementing Al into the accounting system as the selection of effective software and the need to monitor the updating of programmes are described in (Pravdiuk et al. 2022). The researcher (Pilevych, 2020) described the first stages of implementation. These steps are complex, require a lot of attention and effort. A number of researchers note that the successful integration of Al into accounting requires training and brings the accountants' competencies into line with modern requirements (Lelyk et al. 2022). The results of (Megits et al. 2022) show that the current profiles of accountant competence do not meet the latest requirements for business analytics competencies. These conclusions are consistent with the findings of (Xu et al. 2021), which describe the problem of the lack of specialists of the appropriate level and provides a base of the necessary software. However, the mentioned studies lack recommendations for actions that need to be taken to ensure a safe and effective transition. Instead, Damerji and Salimi (2021) provide some important recommendations that ensure a more effective implementation of Al in accounting. In particular, the researchers found a significant impact of accounting students' technology readiness on the successful Al integration into accounting.
As for the prospects for the impact of Al on the accounting profession itself, researchers are inclined to believe that the tasks and skills of the profession will change significantly in the coming decade. In particular, Al will take over a significant share of the tasks of an accountant (Leitner-Hanetseder et al. 2021). At the same time, other researchers (Riinawati, 2021) believe that Al, given the stereotyped thinking, lack of independent thought and professionalism, will not be able to completely replace an accountant. Other researchers are working to eliminate the shortcomings of Al. For example, using Explainable Al methods Zhang et al. (2022) seek to solve the problem of the lack of explanation of Al results.
The researchers who have shown the Al application in the financial and economic analysis of an enterprise's activities reached valuable conclusions. The work (Bilous et al. 2023) reproduces the results of the analysis of financial and economic activities using Al. The work is practical, shows positive trends, but does not sufficiently describe the advantages of using Al by different companies, their experience and mistakes. The study (Zhylin, M. 2024) shows data analytics, inventory control, analysis of costs and deviations in the system's operation, which is the basis for making investment decisions. The researchers (Kulynych et al. 2020) show the possibilities of identifying problems, improving management processes, planning a budget, calculating costs, describing development plans, etc. Han et al. (2023) noted the possibilities of Al for recognizing and applying patterns to expand decision-making. However, all issues are presented somewhat one-sidedly in the studies. There are no practical recommendations and concepts for a holistic approach to generalized experience in building modern information technologies and programmes.
Specific benefits of using Al in accounting, supported by quantitative calculations based on the results of a survey, are provided in (Varzaru, 2022). The researchers found that the implementation of Al allows for significant reduction of processes and improvement of the use of accounting information. Based on the survey conducted by other specialists, Bakarich & O'Brien (2021) also found that Al has not yet had a significant impact on accounting at the time of writing their work. In particular, this applies to robotic process automation (RPA) and machine learning (ML). However, according to the results of the researchers, significant changes are expected in the future. Rane (2023) analysed the use of ChatGPT as an example of generative Al (GenAl) in accounting. The researchers identified such benefits as automation of data entry, categorization and creation of reports, reduction of errors and operational costs. At the same time, the work lacks a quantitative analysis of the impact of the implementation of Al on specific aspects of accounting activities and determination of the level of effectiveness of innovations.
Although Al cannot completely replace humans, it is capable of planning, forecasting, calculating profitability, financial performance of a company, identifying weaknesses, drawing conclusions, and making a development forecast. The need for further research is determined by continuous Al development, which provides ample opportunities for improving the efficiency of accounting. This study contributes to the existing knowledge on the Al use in accounting by analysing its impact on specific aspects of accounting activities and assessing efficiency.
2. Methodology
2.1. Research Design
The preparatory stage of the study included the selection of companies, indicators, and data formatting for analysis. The main stage involved the analysis of the selected indicators before and after the Al implementation using a number of scientific methods. The final stage involved the evaluation of the obtained data.
2.2. Sample
To assess the effectiveness of implementing Al in accounting, a sample of companies was formed for the study. The total number of companies considered for the sample was 14 companies. The companies were selected based on the international status of the company and the duration of Al-based accounting. Such internationalclass companies with branches in Ukraine, Romania, Poland, the USA, Germany, Colombia, Italy, Spain and Chile include SoftServe, Infopulse, Intellias, GlobalLogic, and Sigma. This list also includes Data Science UA, Lemberg Solutions, Artelogic, Innovecs, Toptal, Bayesian Health, Coinbase, Gigster, and GlobalLogic Germany GmbH. All of these companies implemented Al-based programmes. The selected number of companies is sufficient for the study. These companies used Docyt Al and BotKeeper-based accounting programmes.
The efficiency of the accountant was assessed by using the selected quantitative indicators of labour productivity and process optimization. They include the average number of input data entries, the speed of processing banking transactions, the number of types of banking transactions. These data were collected and calculated from standard accounting programmes SAP, M.E. Doc and BAS Accounting. However, one cannot judge the accountants efficiency by quantitative indicators alone. It is necessary to take into account qualitative indicators, such as the correctness of data entry, the complexity of data processing, the typicality and monotony of work. The following indicators served as qualitative indicators of the work performed: the number of errors made, the repeatability and uniformity of errors.
2.3. Methods
The study employed the method of comparing the obtained data on the efficiency of the enterprise using traditional methods and using Al. Traditional methods include manual accounting, the use of Excel tables, and classic accounting programmes. The programme for traditional accounting (SAP, M.E. Doc and BAS Accounting) recorded the receipt of quantitative data by time and the number of registrations. The total number of data registrations or banking transactions was determined per day and per reporting period. The final result was the percentage of registrations that met the established standards and time. The number of errors made when entering data was also recorded. The registration time is considered to be the period from the moment the data entered the system to the moment they were processed by the accountant or Al. The accuracy of information recording was deduced from the data on the number of errors made, repeatability and uniformity of errors in accordance with the amount of incoming information flow. The indicator of process optimization and efficiency of the enterprise was compiled based on data from the SoftServe company, which was collected from the moment Al was introduced.
Qualitative indicators, such as the correctness of data entry, the complexity of data processing, were determined as the ratio of the number of errors to the total volume of transactions, taking into account the indicators of typicality and monotony of work, which means the multiple repetition of monotonous short-term operations, actions, cycles. This indicator was introduced for the work of an accountant based on programmes without Al, as the typicality and monotony of work disappears with the introduction of Al-based programmes. The accountants work was assessed during the year from October 2022 to October 2023 to cover all accounting periods.
Using the method of comparison relative to the observed indicators made it possible to preliminarily assess that the efficiency for each of the studied indicators has significantly increased. Statistical analysis of the indicators clarified the increase in efficiency. The findings obtained through graphical cause-and-effect analysis gave grounds for building a causal cyclic diagram, which helped to show the relationships between the indicators and the direction of the relationship. The model built using the linear regression method helped to determine the impact of indicators on increasing productivity, expressed through the transaction processing time, as well as predict this indicator.
2.4 Instruments
The data were collected from SAP, M.E. Doc and BAS Accounting programmes and company reporting. The effectiveness of Al implementation was studied based on Docyt Al and BotKeeper software. StatPlus Pro for Windows and Excel was used for calculations and statistical data processing.
3. Results
Al-based programmes are advisable to implement for solving monotonous tasks of the same type or for nonstandard tasks with high complexity. Al-based programmes and tools solve a number of issues with fairly high efficiency. Table 1 presents the results of a study on the implementation of Al-based software Docyt Al in accounting in a number of companies.
Table 1 shows that Al-based programmes have a high level of efficiency, reliability, and reproducibility. The number of errors has been reduced to almost zero, work efficiency has been increased, all incoming information is processed on time, quickly, thoroughly, communication is being established, communication with clients is being carried out, and paperwork with partners is being carried out. Table 2 is proposed below to assess how much productivity has increased with the implementation of Al, where the change in indicators in percentages is determined for each company. The Table does not reflect indicators that were already presented in Table 1 in percentage terms.
Table 2 shows that the number of transactions per day (month) has increased for all studied companies, but the growth varies depending on the specific company. For example, for Infopulse, the increase in the number of transactions per month is 209%, for Bayesian Health - about 50%. The time and complexity of processing transactions for all companies have decreased by more than 99%, the number of errors has decreased by 96.5698.45% for most companies, except for Gigster. This is the only company where this indicator has increased by 44.44%, which could potentially be determined by inefficiency or errors in the automation process. Having these data, it is possible to calculate how many employees in the accounting department are needed on average to perform standard transactions without Al and with the Al (Table 3). According to the table, this indicator is 18 (17.43) people without Al and 1 person (0.03) with Al. In hours per week, the average reduction in transaction processing time per week with Al is 696.26 hours.
The obtained results were visualized by building a causal cyclic diagram (Figure 1). The diagram reflects the relationship between the studied indicators. The "+" sign on the relationship line means that the increase (decrease) of one indicator is accompanied by an increase (decrease) of another, that is, the relationship is direct, "-" indicates an inverse relationship between the indicators.
The changes that had the greatest impact on the change in the productivity of accounting systems in companies which implemented Al were assessed through a regression analysis. The transaction processing time was used as a key productivity indicator, which was a dependent variable in the analysis. The independent variables were the complexity of data processing and the correctness of data entry. The causal cyclic diagram shows that these indicators are causally related to the transaction processing time. The diagram shows that the increase in processing complexity is accompanied by an increase in processing time (and vice versa), meaning that the relationship is direct. In turn, an increase in the correctness of entry reduces the processing time of operations, which indicates an inverse relationship between the indicators. The indicators of monotony, the number of errors, the percentage of registrations, and the number of processed operations were not included in the model due to insufficient specifics and/or multicollinearity. As shown in the diagram, these indicators do not interact with the number of processed operations through clear cause-and-effect relationships. They mainly affect variables already included in the model or are the result of reducing transaction processing time. Therefore, these indicators do not provide the model with reliable information to explain the variation of the independent variable and were not considered in it.
The chosen approach to building the regression model allowed to obtain a fairly high-quality model. The correlation coefficient was 0.943, which indicates a strong positive relationship between the dependent and independent variables. In particular, this indicates the importance of the factors included in the model for predicting the number of processed transactions. The coefficient of determination was 0.888, and the adjusted coefficient of determination was 0.879. This indicates that about 88% of the variation in the processing time of operations can be explained by the independent variables included in the model. Table 4 contains the results of the ANOVA analysis.
The Significance F and F-statistic values in Table 4 indicate the statistical significance of the constructed regression model. The regression sum of squares (SS Regression = 5505398486.07) accounts for a significant proportion of the total variance (SS Total = 6196598444.11), and therefore the model explains well the variation in the number of processed transactions. The residual sum of squares SS Residual = 691199958.03 indicates the proportion of unaccounted variation. Table 5 presents the regression results.
According to the results of the regression analysis, both independent variables included in the model are statistically significant at p<0.05. The regression coefficient for data processing complexity indicates that an increase in the complexity indicator by one unit causes an increase in the transaction processing time by 592.69 seconds. Each percentage increase in the data entry accuracy contributes to a decrease in processing time by 5135.51 seconds. The regression model has the form:
Transaction processing time = 511.7 + 592.69 · Data processing complexity - 5135.51 · Data entry accuracy.
The obtained results allow us to better understand which factors affect the increase in the efficiency of the accounting system with the Al implementation. The calculated regression coefficients give companies a clear idea of whether the Al implementation in the companies' activities meets their expectations regarding increased productivity. The constructed model can be used to predict the increase in productivity of companies with the Al implementation.
4. Discussions
All over the world, there is an active introduction of smart technologies into economic activities. The use of many digital practices and programmes has shown good results, which is reproduced in the works of researchers (Savkiv, Kuzmin 2023; Lysenko et al. 2024). The results of the author's study are consistent with the conclusions (Bilous, Kundeus, 2023; Pilevich, 2020), which proved the effectiveness of the Al introduction in accounting. Our study confirm the opinion of (Megits et al. 2022; Pravdyuk et al. 2022), which showed that companies where Al was introduced develop more progressively and have more opportunities.
The advantages of using Al are shown in many studies through the use of questionnaires and interviews. As in the author's study, Várzaru (2022) proved that Al contributes to a significant reduction in accounting processes. Abdullah and Almagtari (2024) concluded that Al in accounting allows to increase efficiency, accuracy and improve decision-making capabilities. Emetaram and Uchime (2021) noted that the Al implementation significantly increases the accountants' productivity. Judging by the mentioned studies, the Al effectiveness of is confirmed in the practice of countries with different levels of development - Romania, Saudi Arabia, Nigeria. However, the methods used by the researchers have certain limitations caused by the subjectivity of the respondents' views. Unlike these studies, the quantitative impact of Al on specific aspects of accounting was confirmed in the author's study by calculating the percentage of efficiency increase. Moreover, regression analysis identified the degree and direction of the influence of individual efficiency indicators on increasing work productivity.
The results of some studies contradict the author's study. In particular, Bakarich and O'Brien (2021) and Gongalves et al. (2022) noted that the digitalization of accounting is only at the initial stage and has a minor impact on the main processes. At the same time, the author's work found that the Al technologies implemented by the studied companies are already having a significant impact on efficiency. The mentioned studies took into account the practice of developed countries, such as the USA and Portugal, so the found differences cannot be explained by the insufficient level of technological development in the respective countries. Instead, the differences can be explained by different approaches to sampling: the author studied large international companies, while the mentioned studies focus on small businesses. This assumption is confirmed by the work of Nóbrega et al. (2023), who noted the lack of potential of small and medium-sized enterprises to implement Al because of weak financial capacity.
The practical application of the author's findings is to quantify the efficiency gains from Al implementation. The regression model obtained in the work allows predicting efficiency gains after Al implementation. This information may be useful for enterprises that plan to use Al in their operations.
4.1. Limitations
The limitations of this study are determined by the difficulties of development in recent years, which is associated with the pandemic. It should be noted that the sample of companies was formed on the basis of their willingness to participate in the study and reluctance to submit their data for analysis. Besides, the software package was selected for the study based on the programmes that the companies currently work with.
4.2. Recommendations
The obtained results give grounds to provide the following recommendations:
- Increasing the data entry accuracy and reducing the processing complexity significantly reduce the time for processing operations. Therefore, it is advisable to give preference to solutions that include automatic verification, error correction and data structuring when implementing Al;
- Successful implementation of Al depends, among other things, on the ability to use new technologies. Therefore, the implementation of training and education of personnel is an effective solution for increasing efficiency.
Conclusions
Taking into account the undisclosed aspects of the problems of Al implementation by other authors, the work revealed the potential of Al for fast, reliable and timely processing of accounting information. The effectiveness of Al implementation was proven based on the study of several programmes. It was determined that the implementation of innovative technologies based on Al in the accounting system can significantly increase the efficiency of accounting activities, affecting labour productivity.
The work found that 18 accounting department employees on average are needed in the studied companies to perform standard operations without Al. With Al, 1 person can handle this amount of work. The average reduction in transaction processing time per week with Al is 696.26 hours. It was also found that the implementation of Al by large companies affects productivity, expressed through the indicator of the transaction processing time. The constructed regression model demonstrates that this indicator is significantly influenced by such indicators as the data processing complexity and the data entry accuracy. It was found that by reducing the complexity of processing by one unit, the processing time of transactions is reduced by 592.69 seconds. Each percentage increase in the correctness of data entry contributes to a reduction in processing time by 5135.51 seconds.
The determined percentage changes in the efficiency of various aspects of accounting after the Al implementation allow companies to determine whether the implementation of Al meets their goals. The regression model obtained in the work makes it possible to predict the increase in efficiency after the Al implementation. This could be useful for businesses that plan to use Al in their operations. Further research could be aimed at determining the impact of risks on the effectiveness of Al implementation, such as data loss, cyberattacks, etc.
Credit Authorship Contribution Statement
Mohammad Ahmad Alnaimat: Conceptualization, Investigation, Methodology, Project administration, Software, Formal analysis, Writing - original draft, Supervision, Data curation, Validation, Writing - review and editing, Visualization, Funding acquisition;
Inna Korsun: Conceptualization, Investigation, Methodology, Project administration, Software, Formal analysis, Writing - original draft, Supervision, Data curation, Validation, Writing - review and editing, Visualization, Funding acquisition;
Kostiantyn Lutsenko: Conceptualization, Investigation, Methodology, Project administration, Software, Formal analysis, Writing - original draft, Supervision, Data curation, Validation, Writing - review and editing, Visualization, Funding acquisition;
Oleksandr Khodorkovskyi: Conceptualization, Investigation, Methodology, Project administration, Software, Formal analysis, Writing - original draft, Supervision, Data curation, Validation, Writing - review and editing, Visualization, Funding acquisition;
Mykyta Artemchuk: Conceptualization, Investigation, Methodology, Project administration, Software, Formal analysis, Writing - original draft, Supervision, Data curation, Validation, Writing - review and editing, Visualization, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Declaration of Use of Generative Al and Al-Assisted Technologies
The authors declare that they have used generative Al and Al-assisted technologies in the writing process before submission, but only to improve the language and readability of their paper and with the appropriate disclosure
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