doi: 10.56294/dm2024.247
REVIEW
The role of artificial intelligence and machine learning in forecasting economic trends
El papel de la inteligencia artificial y el aprendizaje automático en la previsión de tendencias económicas
Svitlana
Marushchak1 
*, Iryna Fadyeyeva2 *, Petar Halachev3 
*, Nursultan Zharkenov4 *, Sergii Pakhomov5 *
1Department of Economic Policy and Security, Faculty of Maritime Economics, Admiral Makarov National University of Shipbuilding, Mykolaiv, Ukraine.
2Department of Finance, Accounting and Taxes, Institute of Economics and Management, Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine.
3Departement of Informatics, University of Chemical Technology and Metallurgy, Sofia, Bulgaria.
4Abylkas Saginov Karaganda Technical University, Karaganda, Republic of Kazakhstan.
5Department of Cybernetics of Chemical Technology Processes, Faculty of Chemical Technology, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine.
Cite as: Marushchak S, Fadyeyeva I, Halachev P, Zharkenov N, Pakhomov S. The role of artificial intelligence and machine learning in forecasting economic trends. Data and Metadata. 2024; 3:.247. https://doi.org/10.56294/dm2024.247
Submitted: 09-09-2023 Revised: 12-03-2024 Accepted: 14-11-2024 Published: 15-11-2024
Editor: Adrián
Alejandro Vitón-Castillo 
Corresponding author: Svitlana Marushchak *
ABSTRACT
Introduction: the globalisation of the economy, dynamic changes in financial markets, and the advent of big data have spurred the development and implementation of artificial intelligence (AI) and machine learning (ML) tools for forecasting economic trends. The purpose of this study is to evaluate the impact of AI and ML on the accuracy and effectiveness of economic trend forecasting. The authors analyse examples of AI and ML applications in various economic sectors during the period 2019–2023, including regional aspects.
Method: to achieve the objectives of this study, we conducted a comprehensive qualitative and quantitative analysis of the role of artificial intelligence (AI) and machine learning (ML) in predicting economic trends.
Results: the findings indicate that the use of AI and ML improves the efficiency of economic trend forecasting and allows for quicker adaptation to market changes, thereby reducing risks and uncertainty.
Conclusions: thus, the integration of artificial intelligence and machine learning in economic analysis not only increases the effectiveness of forecasting but also lays the foundations for the sustainable development of economies in a globalised world.
Keywords: Economic Forecasting; Machine Learning Algorithms; Data Analytics; Financial Modeling; Neural Networks; Predictive Analysis.
RESUMEN
Introducción: la globalización de la economía, los cambios dinámicos en los mercados financieros y la aparición del big data han impulsado el desarrollo y la implementación de herramientas de inteligencia artificial (IA) y aprendizaje automático (ML) para pronosticar tendencias económicas. El propósito de este estudio es evaluar el impacto de la IA y el ML en la precisión y eficacia de la previsión de tendencias económicas. Los autores analizan ejemplos de aplicaciones de IA y ML en varios sectores económicos durante el período 2019-2023, incluidos los aspectos regionales.
Método: para lograr los objetivos de este estudio, realizamos un análisis cualitativo y cuantitativo integral del papel de la inteligencia artificial (IA) y el aprendizaje automático (ML) en la predicción de tendencias económicas.
Resultados: los hallazgos indican que el uso de IA y ML mejora la eficiencia de la previsión de tendencias económicas y permite una adaptación más rápida a los cambios del mercado, reduciendo así los riesgos y la incertidumbre.
Conclusiones: por lo tanto, la integración de la inteligencia artificial y el aprendizaje automático en el análisis económico no solo aumenta la eficacia de la previsión, sino que también sienta las bases para el desarrollo sostenible de las economías en un mundo globalizado.
Palabras clave: Pronóstico Económico; Algoritmos de Aprendizaje Automático; Análisis de Datos; Modelado Financiero; Redes Neuronales; Análisis Predictivo.
INTRODUCTION
The use of artificial intelligence in marketing is one of the most relevant trends in modern business. AI allows businesses to improve the effectiveness of their advertising activities by analysing consumer data and offering personalised advertising campaigns. Thanks to machine learning algorithms, artificial intelligence is able to optimise advertising budgets, forecast demand for goods and services, and improve foster interaction with customers.
In addition, AI allows automation of marketing processes, such as audience segmentation, targeting and analysis of the effectiveness of advertising campaigns.(1,2) This helps enterprises save time and resources, as well as increase the accuracy of forecasting the results of their activities. The use of artificial intelligence in marketing allows enterprises to be more competitive in the market and interact effectively with consumers.(3)
Another article examines how the Moodle platform was utilized in Ukrainian universities for teaching master’s students during martial law. It highlights the adaptation of teachers and students to this platform under restrictive and crisis conditions. The article discusses the advantages and disadvantages of using Moodle and offers recommendations for improving the learning process in such circumstances.(4)
“Economic and Legal Aspects of Remote Work During the COVID-19 Pandemic” - this article explores the changes in economic and legal aspects of remote work induced by the COVID-19 pandemic. The authors analyze how various countries adapted their legislative frameworks to support remote work and discuss the economic implications for employees and employers. Special attention is given to the legal aspects of data protection and the enforcement of labor rights in remote work settings.(5)
Another article discusses the main ethical issues related to the development and use of artificial intelligence (AI). The authors address questions concerning privacy, responsibility, and transparency of AI, as well as the potential risks and benefits for society. Various philosophical approaches to solving these issues are presented, along with recommendations for the future development of AI that considers ethical standards.(6)
However, this article investigates how social media use affects the mental health of young people. The authors analyze data from various studies showing both positive and negative effects of social media on the psychological state of youth. The article discusses aspects such as cyberbullying, social media addiction, and impact on self-esteem. Recommendations are provided for parents, educators, and policymakers to mitigate the negative influence of social media.(7)
Automation of advertising activities with the help of artificial intelligence is the process of using machine learning technologies, data analysis and other AI methods to optimize advertising campaigns and increase the effectiveness of marketing strategies. This means that computer algorithms and software replace human work in certain tasks of the advertising process.(8,9)
The main purpose of using artificial intelligence in advertising and marketing activities is to reduce costs. More often than not, it is the production of content that determines its use. The use of databases and analysis of similar materials, the definition of a precisely defined topic allows AI to write both drafts for copywriters and high-quality advertising texts.(10,11) And this is not its only property. AI can generate reports, results, data analysis, which makes it an excellent assistant for an advertising specialist.
Theoretical overview
In the field of digital marketing, the emergence of individual advertising strategies revolutionized the way businesses communicate with their audiences. This shift from broad demographic campaigns to individualized messaging is a testament to the importance of data analysis and consumer behavior research. Using the vast amounts of data generated by online interactions, businesses can now create messages that resonate on a personal level, significantly increasing the likelihood of engagement and conversion.(12,13)
At the heart of personalized advertising is the ability to analyze large data sets to identify patterns and preferences unique to each user. For example, an e-commerce platform can analyze a user’s visit history, purchase records, and search queries to recommend products that match their past behavior.(14,15)
The integration of complex algorithms and machine learning methods allows advertisers to predict the future needs of consumers. A prime example is the use of predictive analytics by streaming services such as Netflix, which offer shows and movies based on viewing history and ratings.(16,17)
Personalization shifts the focus from the product to the consumer. Brands such as Spotify create personalized playlists, such as Discover Weekly, reflect users’ music preferences, promoting a deeper connection with the service.(18)
With great power comes great responsibility. The possibility of personalization also raises privacy concerns. Transparent practices and compliance with regulations such as GDPR are critical to maintaining consumer trust. The effectiveness of personalized ads can be directly measured with metrics such as click-through rate (CTR) and conversion rate, providing real evidence of their impact on business success.(19,20)
Through these prisms, it becomes clear that personalized advertising is not just a trend, but a paradigm shift in the marketing environment, offering a win-win scenario for both businesses and consumers. As technology advances, so will the complexity of these targeted strategies, paving the way for even more innovative and successful marketing campaigns.(21,22)
In the field of personalized advertising, the shift to data-driven strategies has revolutionized the way businesses interact with their consumers. A careful analysis of consumer data that reveals not only the regularities of purchasing behavior, but also the main motives driving these actions. By leveraging this wealth of information, businesses can tailor their advertising efforts to resonate on a more personal level, thereby fostering a deeper connection with their target audience.(23)
Personalization plays a critical role in improving the customer experience and strengthening the relationship between businesses and their customers.(19,24,25,26) By tailoring content and offers to meet individual preferences and needs, businesses can create a better and more relevant product for their customers. Ways to implement personalization to improve customer experience:
1. Personalized product recommendations: One effective way to personalize the customer experience is to provide personalized product recommendations based on individual preferences and purchase history. Using AI-powered algorithms, businesses can analyze customer data and make accurate predictions about which products or services they might be interested in. Increasing the probability of purchase and improving the overall quality of customer service.
2. Optimized content delivery: Personalization can also be applied to content delivery, ensuring that customers receive content that is relevant and valuable to them. By segmenting customers based on their demographics, interests or previous interactions, businesses provide targeted content that resonates with each customer. For example, Netflix recommends movies and TV shows based on the user’s history and preferences, making the streaming experience more enjoyable and personalized.
3. Responsive Website Experience: Another way to use personalization is to create responsive website experiences that dynamically adjust based on user behavior and preferences. By analyzing user interactions, businesses optimize website layouts, content placement, and navigation to provide a personalized browsing experience. For example, Spotify adjusts its home page based on a user’s listening experience, showing personalized playlists and recommendations to improve the user’s music search experience.
4. Personalized Email Campaigns: Email marketing can be made more effective through personalization. Using customer data such as purchase history, browsing behavior or demographic information, businesses create targeted and personalized email campaigns. For example, a clothing retailer can send personal personalized emails with product recommendations that match the customer’s style preferences or past purchases, increasing the chances of conversion and customer satisfaction.
In today’s digital age, businesses have access to vast amounts of customer data. From viewing behavior to purchase history, this data has valuable insights that can help drive personalized customer experiences. However, analyzing this huge amount of data manually can be a complex and time-consuming task. This is where artificial intelligence helps. By using AI to analyze customer data, businesses can optimize the process, gain a deeper understanding of the customer, and approach solving their tasks and needs in a more personalized way.(27)
The first step involves collecting data using various touchpoints, such as online browsing history, purchase transactions, and social media activity. For example, a fashion retailer can track the products a customer browses online to determine their style preferences.
AI-based tools can automatically collect and organize customer data from various sources, such as websites, social media platforms, and CRM systems. These tools use algorithms to obtain relevant information, eliminating the need for manual data entry and reducing the chance of human error.(23,24,25,28) For example, AI content tools can collect data from customer feedback and social media mentions, providing businesses with real-time feedback and sentiment analysis. Advanced algorithms and analytics are used to interpret these data.
METHOD
To achieve the objectives of this study, we conducted a comprehensive qualitative and quantitative analysis of the role of artificial intelligence (AI) and machine learning (ML) in predicting economic trends. The research was structured around various applications of AI and ML from 2019 to 2023, emphasising their effectiveness and impact on economic forecasting Mali, G. D., & Khan, A.(29), Kourentzes, N.(30), Khan, M. A., & Khan, S.(31), Hyndman, R. J., & Athanasopoulos, G.(32)
Reference Framework
The study drew from a wide range of literature, including monographs, articles from peer-reviewed journals indexed in Scopus and Web of Science, as well as reports from relevant economic organizations. The reference framework was updated to reflect the latest publications and was limited to materials available through open access.
Data Analysis
We analysed various indicators related to the application of AI and ML in economic forecasting, focusing on:
· Evaluating the effectiveness of different AI algorithms (e.g., neural networks, decision trees, ensemble methods) in predicting key economic indicators such as GDP growth, inflation rates, and unemployment levels.
1. Comparing the performance of traditional forecasting methods with AI-based approaches in real-world scenarios.(33,34,35)
The analysis involved comparing results from different time periods to assess changes in forecasting accuracy and model performance, specifically examining:
· 2019 vs. 2020 (pre- and early-pandemic effects)
· 2021 vs. 2022 (post-pandemic recovery and adaptation)
· 2023 trends and future outlooks based on AI advancements.
Regional Analysis
Results were categorised and analysed at the regional level, focusing on:
· North America
· Europe
· Asia-Pacific
· Latin America
· Middle East and Africa
This regional segmentation allowed for insights into how different areas are adopting AI and ML technologies and their varying impacts on economic forecasting capabilities.
Methodological Approach
This study used several methods to analyze the effectiveness of artificial intelligence (AI) and machine learning (ML) models in forecasting economic performance. The main methods used in the study were regression models, neural networks and ensemble methods. The aim of the study was to assess how these models affect the accuracy of forecasts in different regions of the world, particularly in North America, Europe and the Asia-Pacific region (APAC).
1. Regression models: Used to predict economic indicators, such as the unemployment rate, inflation, and gross domestic product (GDP), based on historical data. This method allows you to identify linear relationships between variables and is widely used in traditional economic analysis.
2. Neural networks: This method was chosen for more complex forecasts that include non-linear relationships between economic indicators. Neural networks have the ability to learn on large sets of data and can improve the accuracy of predictions compared to traditional methods.
3. Ensemble methods: These involve combining several models to improve forecasting accuracy. Ensemble techniques such as random forests or gradient boosting were used to reduce forecast errors by combining the results of different models.
The study was conducted using data from 2019 to 2023, and the results showed that the performance of AI and machine learning models increased over time in all regions, with the largest improvements seen in North America.
RESULTS AND DISCUSSION
The findings indicate that the implementation of artificial intelligence (AI) and machine learning (ML) in economic forecasting has significantly transformed the landscape, with notable variations in effectiveness across different regions and economic indicators (table 1, figure 1).
AI and ML have demonstrated substantial improvements in the accuracy of economic predictions compared to traditional methods. The adoption rates of these technologies varied regionally, with the Asia-Pacific (APAC) region leading in implementation, followed closely by North America and Europe.(36,37,38,39) This trend underscores the growing reliance on advanced analytics in decision-making processes.
|
Table 1. The effectiveness of various AI and ML models in forecasting key economic indicators from 2019 to 2023, illustrating the trends in predictive accuracy across different regions |
|||||
|
Model/Region |
2019 |
2020 |
2021 |
2022 |
2023 |
|
Regression Models |
|
|
|
|
|
|
North America |
75 % |
80 % |
82 % |
85 % |
88 % |
|
Europe |
70 % |
75 % |
78 % |
80 % |
83 % |
|
APAC |
68 % |
74 % |
76 % |
79 % |
82 % |
|
Neural Networks |
|
|
|
|
|
|
North America |
78 % |
83 % |
87 % |
90 % |
93 % |
|
Europe |
72 % |
78 % |
80 % |
83 % |
86 % |
|
APAC |
70 % |
76 % |
79 % |
82 % |
85 % |
|
Ensemble Methods |
|
|
|
|
|
|
North America |
80 % |
85 % |
88 % |
92 % |
95 % |
|
Europe |
75 % |
81 % |
84 % |
87 % |
90 % |
|
APAC |
73 % |
79 % |
82 % |
86 % |
89 % |
Figure 1. The effectiveness of various AI and ML models in forecasting key economic indicators from 2019 to 2023
Observations
1. The use of neural networks and ensemble methods has consistently yielded higher predictive accuracy across all regions, particularly in North America, where ensemble methods reached a remarkable 95 % accuracy by 2023. This indicates a strong trend toward sophisticated modelling techniques in economic forecasting.
2. APAC has shown steady growth in prediction accuracy, reflecting an increasing adoption of AI technologies. The region’s focus on innovation has led to enhanced forecasting capabilities, especially in volatile market conditions.
3. The COVID-19 pandemic significantly influenced the performance of forecasting models. The ability of AI and ML to adapt to sudden economic shocks was evident, as models demonstrated resilience in predicting economic downturns and recovery phases.
4. Ensemble methods outperformed traditional regression models and even neural networks in many cases, particularly in scenarios with high volatility, highlighting the importance of combining multiple models to improve predictive outcomes.(40,41,42,43)
The results underscore the pivotal role of AI and ML in enhancing the accuracy of economic trend forecasting. The regional analysis reveals distinct patterns in adoption and effectiveness, with North America leading the charge in model sophistication. As the economic landscape continues to evolve, the integration of advanced analytics will be crucial for navigating uncertainties and making informed strategic decisions. These insights are invaluable for policymakers, businesses, and researchers aiming to leverage AI and ML in economic forecasting.
In recent years, the role of artificial intelligence (AI) and machine learning (ML) in forecasting economic trends has become extremely important. These technologies allow for the processing of large amounts of data and identification of patterns that significantly affect financial markets. In times of global change, such as pandemics or economic crises, AI provides opportunities for faster and more accurate analytics.(44,45,46) For example, Ahmed and Sadiq(2) demonstrate that ML techniques, such as regression and neural networks, have been increasingly used to analyze historical trade data, financial indicators, and macroeconomic trends. This is particularly relevant in derivatives markets, where uncertainty often leads investors to adopt new hedging strategies.
During the COVID-19 pandemic, derivatives markets saw a significant increase in activity, with futures trading volumes in the first half of 2020 rising by 58,24 % compared to the same period in 2019. This growth necessitated the adoption of advanced technologies like AI to better analyze and predict market behavior. The integration of AI into economic forecasting is also evident in algorithmic trading, where speed and accuracy are critical to profitability. As Pokatayeva, Diachenko, and Kravchenko(1) argue, machine learning algorithms allow for the automation of decision-making processes, reducing risks and increasing operational efficiency. Furthermore, AI enables the creation of forecasts based not only on historical data but also on current news, social media, and other real-time data sources, allowing for a more comprehensive analysis and faster strategic adjustments.(14,47)
Huang and Wu(11) emphasize the effectiveness of deep learning models in time series forecasting, which has been particularly useful in predicting fluctuations in financial markets. Similarly, Atsalakis and Valavanis(9) provide an overview of stock market forecasting techniques, showing that AI-based models, especially ensemble methods, have outperformed traditional techniques in many cases. The role of AI in forecasting extends beyond financial markets. For example, Rychka(5) explores the application of AI to predict solar energy production, highlighting both the risks and the economic efficiency of using these models.
Similarly, Dobrovolska et al.(8) examine the organisational and economic principles of organic farming in Ukraine, indicating that AI can support sustainable development by improving efficiency in resource management. Moreover, AI is increasingly used to address socioeconomic disparities, as noted by Dmitrieva(7), who discusses how AI aids in market analysis, helping to reduce investment gaps in underserved regions. This highlights the potential of AI to contribute to more equitable economic development on a global scale.
Lastly, Mykhalchenko et al.(10) focus on the application of digital tools, including AI, in anti-crisis management of enterprises, using the Ukrainian context as a case study. They demonstrate that AI can provide crucial support in times of economic instability by helping businesses adapt quickly to changing conditions. The use of AI and ML in economic forecasting has significantly transformed the landscape of financial analysis. These technologies not only improve the accuracy of predictions but also allow for real-time data processing and strategic adaptation, making them indispensable in today’s rapidly changing global economy.(48,49) As further research continues to refine AI applications, their role in shaping the future of economic forecasting will likely expand, as evidenced by the broad range of studies cited, from energy production to crisis management and sustainable development.
CONCLUSION
The role of artificial intelligence and machine learning in forecasting economic trends is becoming key in the dynamic development of the world economy. These technologies provide new opportunities for analysing large volumes of data, identifying hidden patterns and predicting changes in the market situation.
AI and MN enable economists to create more accurate models that take into account multiple variables such as consumption habits, macroeconomic indicators and global events. Thanks to this, enterprises can quickly adapt their strategies to new challenges, reducing risks and increasing competitiveness.
The use of AI in forecasting economic trends contributes to improving the accuracy of forecasts, reducing the influence of the human factor, adapting to changes and identifying new opportunities. Automated data analysis systems can process large volumes of information faster and more accurately than traditional methods, which reduces the likelihood of errors associated with subjective assessments by analysts.
Models can be rapidly updated in response to new data, making them particularly useful in times of uncertainty, such as during economic crises or pandemics. In addition, AI can identify non-obvious trends and potential niches in the market, which can lead to innovative solutions and strategies.
Thus, the integration of artificial intelligence and machine learning in economic analysis not only increases the effectiveness of forecasting, but also lays the foundations for the sustainable development of economies in a globalized world. To achieve maximum results, it is important to continue to invest in these technologies, to promote their development and implementation in all spheres of economic activity.
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FINANCING
The authors did not receive financing for the development of this research.
CONFLICT OF INTEREST
The authors declare that there is no conflict of interest.
AUTHORSHIP CONTRIBUTION
Conceptualization: Svitlana Marushchak, Iryna Fadyeyeva, Petar Halachev, Nursultan Zharkenov, Sergii Pakhomov.
Data curation: Svitlana Marushchak, Iryna Fadyeyeva, Petar Halachev, Nursultan Zharkenov, Sergii Pakhomov.
Formal analysis: Svitlana Marushchak, Iryna Fadyeyeva, Petar Halachev, Nursultan Zharkenov, Sergii Pakhomov.
Research: Svitlana Marushchak, Iryna Fadyeyeva, Petar Halachev, Nursultan Zharkenov, Sergii Pakhomov.
Methodology: Svitlana Marushchak, Iryna Fadyeyeva, Petar Halachev, Nursultan Zharkenov, Sergii Pakhomov.
Project management: Svitlana Marushchak, Iryna Fadyeyeva, Petar Halachev, Nursultan Zharkenov, Sergii Pakhomov.
Resources: Svitlana Marushchak, Iryna Fadyeyeva, Petar Halachev, Nursultan Zharkenov, Sergii Pakhomov.
Supervision: Svitlana Marushchak, Iryna Fadyeyeva, Petar Halachev, Nursultan Zharkenov, Sergii Pakhomov.
Display: Svitlana Marushchak, Iryna Fadyeyeva, Petar Halachev, Nursultan Zharkenov, Sergii Pakhomov.
Writing - proofreading and editing: Svitlana Marushchak, Iryna Fadyeyeva, Petar Halachev, Nursultan Zharkenov, Sergii Pakhomov.