doi: 10.56294/dm2024430

 

REVIEW

 

The Use of Big Data in Corporate Accounting and Data Analysis: Opportunities and Challenges

 

El uso de Big Data en la contabilidad empresarial y el análisis de datos: Oportunidades y retos

 

Olga Ievsieieva1  *, Halyna Matskiv2  *, Nataliia Raiter3  *, Oleksandr Momot4  *, Anatolii Shysh5  *

 

1Department of Finance, Accounting and Audit, Faculty of Economics, Ukrainian State University of Railway Transport. Kharkiv, Ukraine.

2Department of Accounting and Taxation, Faculty of Management, Economics and Law, Lviv National Environmental University. Dublyany, Ukraine.

3Department of Business & Trade, Faculty of Management, Economics and Law, Lviv National Environmental University. Lviv, Ukraine.

4Department of Economics, Management, Business, Kyiv International University, Kyiv, Ukraine

5Department of Statistics and Economic Analysis, Economic Faculty, National University of Life and Environmental Sciences of Ukraine. Kyiv, Ukraine.

 

Cite as: Ievsieieva O, Matskiv H, Raiter N, Momot O, Shysh A. The Use of Big Data in Corporate Accounting and Data Analysis: Opportunities and Challenges. Data and Metadata. 2024; 3:430. https://doi.org/10.56294/dm2024430 

 

Submitted: 06-02-2024          Revised: 02-05-2024          Accepted: 20-07-2024          Published: 21-07-2024

 

Editor: Adrián Alejandro Vitón Castillo  

 

ABSTRACT

 

Introduction: the era of Big Data technologies is restructuring corporate accounting, enabling a wide array of dynamic potential. This project explores how Big Data affects financial management, focusing on forecasting, risk management, and technological advances.

Method: this work is informed by a large-scale review of scholarly literature, industry reports, and case studies. Databases like Google Scholar, PubMed, IEEE Xplore, Scopus, and Web of Science were used for data collection. Keywords included Big Data, corporate accounting, financial forecasting, risk management, data analytics, AI in accounting, machine learning in finance, and blockchain technology applied to accounting. The review was structured thematically, focusing on financial forecasting, risk management, and ethical considerations affected by Big Data practices in this domain.

Results: Big Data improves financial forecasting accuracy using historical data, market trends, and consumer behavior analytics. In risk management, Big Data facilitates effective proactive actions through thorough risk evaluation. Emerging technologies are anticipated to automate complex tasks, enhance predictive analytics, and improve the security and reliability of financial transactions.

Conclusions: Big Data holds significant potential for corporate accounting, though challenges such as managerial complexity, data privacy, and expertise requirements for handling large volumes of data remain. The study highlights the importance of flexibility and technological adaptability, as well as specialized skill sets. It calls for continual dialogue and policy development to meet the ethical challenges presented by Big Data/AI, promoting responsible deployment while ensuring fairness. This review contributes to academic discourse and provides strategic guidance for practitioners in the evolving landscape of corporate accounting.

 

Keywords: Financial Forecasting; Risk Management; Data Privacy; Analytical Skills; Technological Advancement.

 

RESUMEN

 

Introducción: la era de las tecnologías de Big Data está reestructurando la contabilidad de las empresas, permitiendo un amplio abanico de posibilidades dinámicas. Este proyecto explora cómo afectan los Big Data a la gestión financiera, centrándose en la previsión, la gestión de riesgos y los avances tecnológicos.

Método: este trabajo se basa en una revisión a gran escala de la literatura académica, informes del sector y estudios de casos. Para la recopilación de datos se utilizaron bases de datos como Google Scholar, PubMed, IEEE Xplore, Scopus y Web of Science. Las palabras clave incluyeron Big Data, contabilidad corporativa, previsión financiera, gestión de riesgos, análisis de datos, IA en contabilidad, aprendizaje automático en finanzas y tecnología blockchain aplicada a la contabilidad. La revisión se estructuró temáticamente, centrándose en la previsión financiera, la gestión de riesgos y las consideraciones éticas afectadas por las prácticas de Big Data en este dominio.

Resultados: Big Data mejora la precisión de las previsiones financieras utilizando datos históricos, tendencias de mercado y análisis del comportamiento del consumidor. En la gestión de riesgos, Big Data facilita la adopción de medidas proactivas eficaces mediante una evaluación exhaustiva de los riesgos. Se prevé que las tecnologías emergentes automaticen tareas complejas, potencien el análisis predictivo y mejoren la seguridad y fiabilidad de las transacciones financieras.

Conclusiones: El Big Data encierra un potencial significativo para la contabilidad empresarial, aunque persisten retos como la complejidad de la gestión, la privacidad de los datos y los requisitos de experiencia para manejar grandes volúmenes de datos. El estudio destaca la importancia de la flexibilidad y la adaptabilidad tecnológica, así como de los conjuntos de competencias especializadas. Hace un llamamiento al diálogo continuo y al desarrollo de políticas para hacer frente a los retos éticos que plantean los macrodatos y la inteligencia artificial, promoviendo un despliegue responsable al tiempo que se garantiza la equidad. Esta revisión contribuye al discurso académico y proporciona orientación estratégica para los profesionales en el cambiante panorama de la contabilidad empresarial.

 

Palabras clave: Previsión Financiera; Gestión De Riesgos; Privacidad De Datos; Habilidades Analíticas; Avance Tecnológico.

 

 

 

INTRODUCTION

The arrival of Big Data, which has brought with it transformational changes in myriad areas - and not the least among these is business accounting & data analysis. This is not only a technological breakthrough, but also strategic redefinition of financial data used for competitive advantage by businesses to compute and analyze finances. Big data, characterized by the volume at which it is generated (volume), its speed of production across various sources and solutions being built with unstructured data (velocity) offers new opportunities to derive insights from large datasets leading to greater strategic decision making or planning than may have been previously possible a generation ago.(1)  Corporate accounting has historically been very detail oriented and focused on compliance-based behaviors. The movement away from traditional accounting methods and towards that of data driven approaches represents a major move towards predictive analytics as well comprehensive financial management.(2) In this sense, the potential of these big data technologies to revolutionize financial reporting has been harnessed in which case real-time analysis and better forecast were attained making risk management as well as decision-making processes more dynamic.(3) The integration of Big Data with corporate accounting is not a seamless process without problems. Sophisticated analytical skills, robust data governance frameworks (4)and experience in managing & analyzing big complex information is required to ensure that findings are both of a high quality as well as reliable. Meanwhile, as the act of making decisions with data becomes more central to organizations across functions and industries, concerns over data security/privacy/ethical implications have emerged. Law frameworks such as the California Consumer Privacy Act (CCPA) - in a US context - and General Data Protection Regulation (GDPR), for example, across United Kingdom and European Union also require that organizations have mechanisms to detect these attacks because they are classified as data breaching.(5)

This paper outlines the landscape of Big Data as it relates to corporate accounting and data analysis. It refers to the opportunities of Big Data in elevating financial reporting precision, risk identification as well as strategic decision making. It also looks into the challenges of Big Data, and this is actually how requirement for special analytic skills comes here along with daunting privacy concerns as well complex data administration. With this study, we have contributed to the discussion around integrating Big Data in corporate accounting. By combining an evidence-based synthesis of academic research, managerial insights and practitioners’ experiences, it offers a roadmap for how firms can leverage Big Data to realize value but also emphasizes compliance with ethics and various data-driven regulations. This question is of interest not only to scholars wishing to expand the intersection between big data analysis and accounting, but also for practitioners grappling with technological changes in accounting. In this article we want to help you understand the role of Big Data in business accounting, discussing a world full of opportunities and revolutions on one side and struggles that should be met at least halfway. Moreover, this study examines possible trends of putting Big Data into practice in accounting function over the years.

 

METHOD

Using a systematic literature review approach, this research studies the opportunities and challenges faced by corporate accounting due to Big Data technologies.

 

Data Collection

The information has been sourced from various research reports, books, journals, white papers and online portals. Literature search databases were Google Scholar, PubMed, IEEE Xplore, Scopus and Web of Science. We used search keywords: “Big Data,” “corporate accounting,” “financial forecasting, “risk management,” “data analytics”, AI in accounting”, machine learning software companies finance” and blockchain technologies.

 

Criteria for selected sources

·      Big Data applications for corporate accounting

·      Works in financial forecasting and risk management.

·      Emerging Trends and Technologies

·      Science journal publications and top-tier industry reports

·      Articles published within the past 5 years to stay updated Articles that did not meet this criterion were discarded in order to develop a focused set of high-quality sources.

 

Data Analysis

The examination is conducted by grouping the collected literature into clusters having common themes surrounding Big Data application with corporate accounting. A lot of this work is to identify themes surrounding areas that are at the heart and core (Key) issues or considerations in relation to Big Data technologies, such as financial modelling/financial forecasting; risk management /risk governance, ethics, and specialized skills necessary for data analytics.

A comprehensive review process was conducted to help identify knowledge gaps in the current literature and recommendations for future research. All inclusion articles were thoroughly reviewed and information contained within appropriately extracted, systematically organized and synthesized to achieve a comprehensive understanding of the field.

 

Research Techniques

The methods of research were comprehensive, systematic searches of keywords in a multitude of academic and industry databases. Review of titles and abstracts for selection criteria and then full-text review to identify eligible studies. Thematic analysis to organize and summarize findings in the literature Cross referencing for the exhaustible inclusion of relevant appraisals and advance trends. The use of these strategies is intended to offer a full and reliable picture about how Big Data technologies may affect corporate accounting.

 

RESULTS

The exploration of Big Data’s integration into corporate accounting unveils a rapidly evolving landscape, where traditional practices are being redefined by the influx of vast, diverse datasets. This section provides a critical overview of the breadth of research dedicated to better understanding Big Data and its effects on accounting, from developing financial forecasts and becoming more agile in risk management (through big data), as well as remaining alive race through managing large-scale transactions. The ability to predict future economic activity is being advised by an increasing emphasis on information governance, digital privacy, skills required. Every featured study reflects different aspects of the transformation that Big Data is having on accounting, providing an overall view what research and progress has been made so far in a dynamic field.

The process of the thematic analysis framework used in this study is displayed in figure 1. It then organizes the literature in this area around broad themes that bear upon corporate accounting. These are financial forecasting, risk management, ethical concerns, data privacy and an argument for specialized skills in data analytics. Sub-themes are listed below each theme to identify specific areas of focus within each category. The framework allows for the systematic structuring and integration of empirical insights gleaned through a review scanning process.

 

Figure 1. Thematic Analysis Framework of Big Data in Corporate Accounting

 

The main themes and sub-themes that were found through thematic analysis are shown in table 1 and figure 2. It makes it easy to see how Big Data technologies have changed different areas of corporate accounting by focusing on specific sub-topics within each main theme.

 

Table 1. Themes and Sub-Themes Identified

Theme

Sub-Themes

Financial Forecasting

Historical Data Analysis, Market Trends, Consumer Behavior

Risk Management

Predictive Analytics, Real-Time Monitoring, Fraud Detection

Ethical Considerations

Data Integrity, Transparency, Accountability

Data Privacy

GDPR Compliance, CCPA Compliance, Data Security

Specialized Skills

Data Analytics Training, Machine Learning, AI in Accounting

 

Figure 2. Word Cloud of themes and sub-temes

 

Figure 2 and figure 3 shows how Big Data technologies have changed financial reporting. It shows how processing data in real time, improving accuracy, and doing a full analysis all help make financial disclosures timelier and more correct. The graph shows how combining structured and unstructured data can give a complete picture of a business’s money situation.

 

Figure 3. Impact of Big Data on Financial Reporting

 

Impact on Financial Reporting

The use case for improving financial reporting accuracy and completeness exemplifies how Big Data technologies are used.(6) The application of Big Data into traditional accounting techniques has improved our understanding of the world.(7) However, it merely adds greater transparency to only informally adjudicated information that is essentially no better than years-of-solitude word of mouth gossip. According to Medeiros et al.(8) there is need to address the barriers to adoption of Big Data in financial reporting by improving data governance structures and addressing a lack of data scientist to interpret complex large datasets.

Yu et al.(9) focusses on how Big Data technologies support instant processing of data and render a great help in financial report preparation. This advance enables them to issue financial statements that are more up-to-date and materially mis-state less information, potentially cutting the time lag between data being generated and reported. As noted by Salijeni et al.(10) Big Data facilitates the combination of dissimilar data forms, from structured financial transactions to unstructured social media streams; painting a richer portrait of fiscal well-being. Still, something skeptic showed De Santis and D’Onza,(11) “which is rather the problem of correct interpretation: this data is immense in quantity but very heterogeneous”. The study highlights the importance of accounting practitioners investing in advanced data analytics abilities to maximise Big Data benefits of improved reporting precision.

 

Works With Conventional Accounting Practices

Mahon(12) calls for the combination of Big Data and traditional accounting practices to be dismissed as an improvement in financial reporting. Their conclusion is that, while Big Data does offer a larger amount of input data, the greatest impact would be felt when it supplements current accounting practice and methods. Quesado and Silva(13) present a work with an integrated perspective where explain very well the use of Big Data in activity-based costing. The authors demonstrate how the integration of Big Data in traditional costing methods can offer better tailor-made cost insights to support strategic decisions.

 

Challenges in Adoption

According to Yarmoliuk(14), knowledge is a key asset for enterprises, and he addresses the challenges faced by organizations in relation to information support as well as an approach which could be used for measurements. The evidence supports the increasing role of information-based resources for effective management in contemporary socio-economic environment. The service level assessment based on the Harrington scale proposed in this paper is a concise model that evaluates support for information from low to high with useful value carried so as to be of practical reference when improving enterprise information management. One of the major challenges is to have a rigorous data governance framework in place with no room for errors. Also, the challenge highlighted by Singh & Sharma(15) that is to fill in skills gap required for understanding intricate Data sets. The authors argue for new training and education programs to ensure that accountants are trained in the data analytics skills required by today’s Big Data environment.

Vdovichena et al.(16) portrays the pacific effect of information as a new wealth that transforms many areas such as economy, environmental and political development. Information is defined as a potent, disruptive and dynamic element in the economy.(17) This research also points some of the key globalisation trends in information use, strategies retrieved by worldwide population and how databases are used to mediate media influence within various communities, thus informing practices around circulation economy formation.(18) The findings could be used in practice by helping enterprises that are interested to enter the global market, understand how contemporary information usage has been increasing and contributing positively for further scientific research. Although Big Data tools have been successfully utilized to solve various accounting problems. Technological issues related to integrating these technologies with traditional accounting information systems. Research emphasizes the requirement to invest heavily in technology architecture and compatible software options for incorporating Big Data analytics simply into monetary statement reporting procedures.

Table 2 outlines the main challenges in adopting Big Data technologies in corporate accounting. It describes the issues related to data governance, skills gap, ethical and privacy concerns, technological integration, and data quality management. This table helps in understanding the obstacles that need to be addressed to fully leverage Big Data in accounting practices.

 

Table 2. Challenges in Adopting Big Data Technologies

Challenge

Description

Data Governance

Ensuring data integrity and security through robust data governance structures.

Skills Gap

The need for specialized training and education programs to equip professionals with data analytics skills.

Ethical and Privacy Concerns

Adhering to ethical standards and privacy regulations while handling sensitive financial data.

Technological Integration

Investing in technology infrastructure and developing compatible software solutions.

Data Quality Management

Ensuring the accuracy and integrity of diverse data types, including structured and unstructured data.

 

Figure 4. Challenges in Adopting Big Data Tecnologies

 

Risk Management and Big Data

The synergy between Big Data and risk management in accounting embodies a changeover from traditional reactive tactics to proactive at times real-time game plans. This is an evolution exemplified by the use of secondary data to support financial risk identification, assessment and management that began when predictive analytics combined with real-time monitoring first associated it. A study by Ibrahim et al.(19) shows how big data fuelled predictive analytics is disrupting accounting risk assessment. When accountants assess patterns and trends, they can mitigate financial risks before resources are spent, incorporating a risk management strategy. Hasan et al.(20) discuss the application of Big Data techniques to conduct real-time surveillance of financial risks. This demonstrates how continuous auditing and monitoring allows the company with Big Data capabilities can quickly identify risk areas, as well as respond to it in a due manner. Azieva et al.(21) investigate the role of information technology in an industry with strategic importance-energy, specifically access to electricity transmission networks across countries. The theme of the paper is revealing realities in central Asia, calling for high-tech standards and conducting reforms at average level of electricity energy generation sector within Kazakhstan as well discussing how emerging trends with IT can increase overall operational efficiency.

 

Predictive Risk Analysis

Dubey et al.(22)   demonstrate the large improvement in risk assessment provided by predictive analytics. By using Big Data, accountants can take a detailed view of old and new financial records which could give them possible predictors for traps. This approach facilitates a more advanced and forward-looking risk management strategy not subject to the limitations of traditional methods which often rely heavily on historical data, i.e., backward looking instead. Big Data analytics has enormous potential to help in identifying risks across multiple areas (such as credit risk, market risk and operational) types with a high degree of accuracy. For example, credit risk models today include a greater number of data fields than in the past that contain non-financial characteristics to more effectively predict defaults. This method was demonstrated in a study performed by Djeundje et al.(23), which nicely demonstrated the potential of integrating non-traditional data features such as consumer online activity and social media engagement with more traditional financial variables. Our study suggested that integrating social information could enhance the overall accuracy of credit risk models substantially. In addition, the Big Data predictive risk analysis in business is realized by Rangaraju(24) who explained that it enables organization to be detected threats even before happening associated with cyber security and regulatory changes, then allowing them realize plan for a manner of mitigating these risks.

Seyedan and Mafakheri(25) explain about how predictive analytics can be used to guess how well a business will do.  Their research indicates that Big Data tools, such as machine learning algorithms, enable more accurate predictions of future financial trends, aiding strategic decision-making. Shah et al.(26) discuss the use of behavioural data in predictive analytics. They suggest that incorporating consumer behaviour data can significantly enhance the accuracy of financial forecasts through the use of directed graph networks. Continuous auditing and monitoring, powered by Big Data technologies, allow firms to detect and respond to risks as they arise, rather than relying on periodic reviews.(27) Real-time risk monitoring involves the use of sophisticated algorithms and data analytics tools to constantly analyze transactional data and operational metrics. This approach is particularly beneficial in areas like fraud detection, where immediate detection and response are crucial. Shuplat et al.(28) explore innovative financing for the reconstruction of principal funds in wood processing enterprises. Employing methods like analysis, synthesis, regression, and internal rate of return, it outlines strategic priorities for woodworking enterprises, forecasting 2025 capital investments in Ukraine and Poland. A study by Priya and Saradha(29) illustrated how real-time analysis of transaction data could effectively identify patterns indicative of fraudulent activities. As Pugna et al.(30), point out, this proactive approach to risk management not only lowers the possible effects of risks but also makes the organization more flexible and stronger overall. It lets companies quickly adapt to changes in the market and their working conditions, reducing the damage that could come from unplanned events.

 

Challenges for Practitioners

When accountants use “Big Data,” it creates some new problems. These problems mostly have to do with managing and quality control of data, as well as the moral and privacy issues that come up when dealing with huge amounts of financial data. Fan and Geerts’(31) study is all about the problems that come up with managing and quality control of Big Data in accounting. To get the most out of Big Data, they stress how important it is to make sure the data is correct and come up with strong data management plans.  One of the significant challenges in applying Big Data in accounting is navigating the ethical and privacy concerns. They stress the need for accountants to adhere to ethical standards while handling sensitive financial data.

Rakhimov(32) explored philosophical and axiological aspects of the moral and ethical dimensions of artificial intelligence (AI) in modern society. The study analyzes ideas and beliefs within contemporary scientific discourse regarding the positioning of AI within the mental and worldview paradigm of the modern world. Drawing on 55 studies from the past 5 years that delve into the correlation between morality and AI, the article aims to determine the relationship between morality and technical advancements. The methodological approach employs general scientific methods, primarily analytical, along with philosophical methods like dialectical and synergistic methods. The results indicate a significant inter-integration of technology and morality in modern society, manifesting in various dimensions such as AI shaping a new moral paradigm and influencing the transformation of existing moral norms.

 

Data Quality and Management

Problems with data management and quality are a part of Big Data accounting. The decision-making process is highly dependent on the quality of the data; thus, it is crucial to ensure that the data is accurate and intact. It highlights the difficulty of dealing with various data kinds, such as organized numerical data and unstructured textual information. To maintain consistency and trustworthiness in the face of such variety, advanced data processing and management approaches are required. Integrating Big Data with preexisting accounting systems is also a significant difficulty. The difficulties in building the foundation for the market for future Internet services are discussed by Cherniaieva et al.(33) Services aimed at people, human knowledge, physical and immaterial things, and the creation of physical assets are all part of the Internet services industry, according to the article. Servers, ISUs, signal transmission, acceleration, data transfer, Internet platforms, and security of user data are all factors in the three-tiered infrastructure study. With helpful insights for navigating the changing Internet services market environment, the suggested plan lays out the necessary conditions for industry reform, change processes, and improvement areas.

The study by Konanahalli et al.(34)   elaborates on the challenges of data integration, noting that discrepancies in data formats, structures, and quality between traditional accounting systems and Big Data sources can create significant technical and analytical hurdles. Additionally, keeping track of such huge datasets needs a lot of computing power and advanced storage options.(35) They stress how important strong IT infrastructure and fast data processing algorithms are for managing and analyzing Big Data in accounting.

 

Ethical and Privacy Concerns

Amonoo Nkrumah et al.(36) uncover the ethical implications and privacy concerns of Big Data in accounting. In their study, Anshari and Sumardi(37) explore about the problems on a larger scale that can cause Big Data if used wrong; financial data could be stolen or tampered with in ways which will harm both people as well as organizations. This highlights the importance for strict data security and ethical consumer protection, ensuring all personal information is kept secure. Privacy challenges are made worse by compliance issues.(38) We examine how the likes of GDPR and CCPA make heavy demands for data privacy & consent amidst a challenging legal chessboard, while driving Big Data into productive use from an accountant perspective. Iliev(39) pays attention to the philosophical considerations for blockchain rules in an environment of this emerging meta-universe. The paper is a thorough examination of the research papers and solution concept regarding how Blockchain can provide one-click sign-in functionality to third-party applications by using decentralized identity tools constructed on top. Results highlight the promise of a metaverse to improve transparency, trustworthiness and accountability in data sharing beyond just providing creative solutions.

Morska et al.(40) explores how modern science and technology enter into the contested terrain of human rights in future. Using a mixture of philosophy, law and technology this book considers the type of challenges that are posed to human rights by technological advances. In this article, evaluate transhumanism and post-humanistic vision of technology to enhance individuality which determines the identity separation on both human beings could respect each other as individuals with equal dignity The authors analyze it with the lens of existing legislative acts, and then argue that AI is a fruit in urgent need for regulation and provide action to counter any potential humanistic violations revealed by technological developments.

 

Trends and Research Directions

The influence of Big Data and integration of AI and ML is about to significantly change the accounting field. In addition to automating accounting tasks, these technologies can deliver deep analytics that have the potential to revolutionize traditional finance. Bifacial solar cells, although not exactly new developments under the industry 4.0 context but as highlighted by Megits et al.(41) In a similar vein, the “Five-Helix” model to facilitate company development in different countries (USA, Azerbaijan, Ukraine and Poland). In its discussion of the innovation system, which is made up of various actors such as “business,” “society,” “state” and or scientific institutes from the one side, and environment on other side. It evaluates Same factors that stimulate or hinder corporate development. With the help of development indicators for different components of this model, enterprises will be better able to evaluate their capability to implement Industry 4.0 successfully and they may even take full advantage of the five-helix consociation model in this case. The results are critical factor in decision-tree framework probably to support the graduation step on identifying potential scenarios of business growth and data transformation.

The rapidly expanding applications of AI and ML in accounting are explored in an article by Cioffi et al.(42). These technologies are expected to simplify repetitious tasks, provide useful insights, and aid in making educated choices in complex accounting scenarios in the future. Integrating unstructured data sources and developing advanced analytical tools to handle Big Data’s complexity and size should be future research priorities in accounting and Big Data.(43)

Zadorozhnyi et al.(44) addresses the challenges of managing non-current assets under modern military-pandemic and crisis conditions, emphasizing the need for optimal utilization. Employing various research methods, including analysis and economic modeling, the study refines the conceptual positioning of “non-current assets” in accounting. It enhances the method of reflecting these assets in financial statements and introduces a new document for fixed assets accounting. Additionally, the article proposes differentiating costs and revenues related to emergencies, providing a comprehensive approach to improve the synchronization of accounting practices with international and national standards, optimizing enterprise activities during crises.

Artificial intelligence (AI) and machine learning are expected to be very useful for figuring out complicated financial data, finding trends and outliers that humans might miss, and helping people make strategic decisions. A lot of people have noticed how AI has changed fields like risk assessment and fraud detection. Vyas,(45) for example, showed that AI can process huge amounts of transactional data in real time. Because of this, AI is better than traditional methods at finding possible frauds and financial irregularities. Machine learning-based algorithms can always learn and change, which makes them better at analyzing data over time. This is very important in ever-changing financial situations where new patterns and risk factors are always appearing.

 

The Future of Big Data in Accounting

Unstructured data sources, like social media feeds, news reports, and even text data from financial reports, should be a big focus of future research in Big Data and accounting. The hard part is making complex analytical tools that can handle and draw useful conclusions from such a wide range of data sets. According to a study by Maheshwari et al.(46) predictive analytics will play a bigger role in the future of Big Data in accounting. Predictive models can more accurately predict financial outcomes when they use both structured and unstructured data. This helps with strategic planning and making decisions. Using innovation management for long-term growth makes it even more important to make sure that operational and strategic approaches are coordinated. It introduces a new method for evaluating the effectiveness of the innovation process under dynamic conditions, utilizing comparative analysis, structural analysis, scientific abstraction, and mathematical formalization. The study underscores the significance of intellectual capital in sustaining competitive advantages and highlights the systemic nature of impact innovations on enterprise activities. The developed mathematical approach is deemed valuable for both enterprise management and academic research in innovation management.(47) Additionally, as per research by Ntoutsi et al.(48), there is a growing need to understand the ethical implications and biases that might arise from Big Data analytics. Ensuring fairness and transparency in data-driven decision-making will be a key focus area for future research.

Verbviska et al.(49) analyze the effects of e-commerce on the creative growth of Ukrainian companies in comparison to international tendencies, and they emphasize on the role that customers, company founders, and the state play in this process. The study makes use of techniques for establishing cause-and-effect relationships, statistical analysis in economics, and comparative analysis. The results define the present status of e-commerce development in Ukraine, including the percentage of people using the Internet, the percentage of people buying things online, and the percentage of GDP that comes from e-commerce. Findings are compared with worldwide statistics to reveal which sectors and online resources are most popular in Ukraine. If company founders want to know what’s happening in the world of online shopping and how to steer their companies in the right path, these findings will be helpful. However, by highlighting the importance of big data in accounting, Shah and Shah(50) develop a plan for online commerce and the export of human capital.

Petchenko et al.(51) examines the current status of digitalization in the Ukrainian accounting sector, considering both global trends and local challenges. Despite regulatory approval, factors such as war conditions and irregular power supply hinder widespread adoption of electronic accounting. The research identifies popular global information technologies and software utilized by Ukrainian accountants, while clarifying relevant legal acts. The advantages of modern information technologies, such as data integrity and remote access, are highlighted, alongside obstacles including inadequate digital infrastructure, insufficient investment, and legislative misalignment with global digitalization trends. Getting these problems solved is necessary to make accounting and business management in Ukraine more efficient.

The new era of Financial Analysis By allowing the masses of disparate data types to be analyzed, financial forecasting has evolved as accountants can draw on details from multiple sources - such as past and present market trends and consumer behavior- when making projections at a factory or institution. This wide array of data inputs results in more comprehensive financial run rate predictions and helps guide strategic planning initiatives within enterprises. Big Data goes beyond just forecasting - it is a sea change in risk management from more traditional, reactive methods to one that is far more proactive. By crunching vast swaths of data, accountants now can catch potential risk factors more quickly and respond with a sharper mitigation tactic. This kind of proactive risk management goes further than addressing financial risks, to bring operational and social/ reputational risks into the arena in which accounting is practiced.

The information produced from Big Data analysis is invaluable in terms of making strategic business decisions. Companies can now be more strategic about where they invest, with data-backed insights informing decisions around mergers and acquisitions, market expansions and product developments. This has expanded the horizons in business strategy where decisions are no longer guided by past perspective and intuition but data analytics. The integration of Big Data into accounting is, however, not without its challenges. The information technology systems required for processing large data sets can be very expensive, and this expense may preclude the use of sophisticated tools and techniques like cloud-based computing. The problems are compounded for data governance and quality assurance, however, due to the challenge of combining these diverse types of information into an understandable to derive meaningful insights or trends from such a dataset requires some fancy processing magic. With Big Data, the stakes are raised even more so when dealing with sensitive financial data - and should be of concern in an increasingly privacy conscious community. When wading through the complicated waters of data privacy legislation such as GDPR and CCPA, adhering to compliance is a must along with your ethical standards. One important sub factor that adds to the complexity of Big Data initiatives is the compliance requirement - financial information must be protected from breaches and illegal access.

The rise of Big Data into the accounting ecosystem further highlights a trend for niche roles: data analyst/machine learning expert. In light of this growing era for big data, Machine learning and AI the reputed educational institutions or professional bodies have started offering courses specialized in this area to address skills gap. The promise with AI and machine learning is that they can automate and simplify complex accounting tasks, which would enhance the overall efficiency of financial predictions. The transparency and immutability of the blockchain, matching system between banks with real time exchange rate to perform trades will change how transactions are recorded for audits in a safe manner like never before.

With the advent of Big Data evolving accounting, it is likely that we will see many more ethical implications and data privacy challenges as focal points in continued debate over how this new form of technology treats our expectations for impacts on society. Responsible and transparent use of these technologies however are critical to maintain trust in the field. The combined features of Big Data and emerging technologies would shape an efficient, accurate, and more informed financial operation in the accounting industry. This momentum of innovation reflects an exciting time in corporate accounting, where progressivism is accompanied by continued commitment to professional ethics.

 

CONCLUSIONS

One of the major changes that has come in accounting as a result of this big data revolution is when Big Data and corporate rankings end up more converging, where Bigger & Better provide true test cases to Ultimate Technologies. This evolution - which includes the shift to predictive analytics and real-time data processing - has yielded dramatic improvements in operational efficiency, but also allows a new proactive modus operandi for financial management. Nonetheless, the implementation of Big Data is fraught with its own unique set of difficulties. Data management complexity, the need to keep data quality high and broad privacy worries must all be carefully dealt with. Secondly, the recognition of a dearth in data analytics expertise also serves to underscore an area that must be addressed within professional education and training. Given such challenges, there needs to be a continuous process of adaptation in technological progress. While to capitalize on Big Data, one needs accounting professionals who keep up with technology. The recommendations for future research in the field will likely improve as it continues to develop, especially with regard to new technologies and analytical methods that are rapidly being adopted across accounting. This is essential to ensure that the corporate accounting field not only remains competitive with technology but uses it as a vehicle for efficiency, accuracy and insight driven tactics.

 

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FINANCING

No financing

 

CONFLICT OF INTEREST

The authors declare that there is no conflict of interest

 

AUTHORSHIP CONTRIBUTION

Conceptualization: Olga Ievsieieva, Halyna Matskiv.

Data curation: Nataliia Raiter.

Formal analysis: Oleksandr Momot.

Research: Olga Ievsieieva, Anatolii Shysh.

Methodology: Halyna Matskiv, Nataliia Raiter.

Project management: Oleksandr Momot.

Resources: Anatolii Shysh.

Software: Oleksandr Momot.

Supervision: Anatolii Shysh.

Validation: Nataliia Raiter.

Display: Halyna Matskiv.

Drafting - original draft: Olga Ievsieieva.

Writing - proofreading and editing: Halyna Matskiv, Nataliia Raiter.

 

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

 

TRANSPARENCY

The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing.