INTEGRATION OF MACHINE LEARNING AND STATISTICAL MODELS IN FINANCIAL PLANNING

Abstract

The integration of machine learning (ML) methods with traditional statistical models in financial planning is becoming a fundamental change, driving the rapid evolution of analytical solutions in modern corporate finance practices. With growing data volumes and increasing economic uncertainty, traditional statistical models are often insufficient for the effective analysis of complex, unstructured, or non-linear data. Meanwhile, machine learning algorithms are highly adaptable, capable of processing large volumes of data in various formats and revealing complex relationships that traditional methods are unable to identify. However, ML methods often lack interpretability, which is critically important in the financial sector. Therefore, by combining these two methodological approaches, the aim is to create hybrid models that are both accurate and interpretable, thus increasing their reliability and applicability in real business situations.
This article aims to examine in detail the possibilities of integrating machine learning (ML) and traditional statistical models in financial planning and to assess how such a methodological combination can improve the accuracy of forecasts, risk assessment, and strategic decision-making. Based on a broad theoretical and conceptual analysis of scientific literature, the main contemporary analytical methods and their areas of application are examined: regression models, ARIMA and GARCH type time series forecasting techniques, neural networks, ensemble algorithms, and various hybrid model structures. Particular attention is paid to the analysis of the advantages and limitations of these methods, as well as their complementarity, in order to understand under what conditions an integrated method can generate the greatest added value in the field of finance.
The results show that the application of hybrid models allows combining the stability and clear interpretation of results of traditional methods with the ability of machine learning to process complex structures, thus achieving significantly more accurate forecasts. Empirical studies confirm that such systems ensure greater reliability of forecasts not only in stable economic periods, but also in times of significant market volatility. Hybrid models are particularly effective in credit risk assessment, corporate bankruptcy probability forecasting, stock price change analysis, and other areas of financial risk management. The ability of these models to integrate multiple data sources (financial indicators, transaction data, textual information) ensures a more comprehensive and accurate assessment of the financial situation. However, the practical combination of ML and statistical methods poses a number of challenges. The biggest obstacle is data quality and the process of preparing it – in the financial sector, data is often fragmented, stored in separate systems, or does not comply with uniform structures. In addition, hybrid models require significant computing resources, advanced data management platforms, and specialists with interdisciplinary knowledge in finance, data science, and statistics. Organizations also face cultural barriers: analysts may distrust black-box algorithms, while managers value model transparency and the ability to justify decisions.
The results of the study show that only a consistent and well-organized data management system, based on clear standards for data quality, accessibility, and storage, provides a solid foundation for the successful integration of hybrid models into financial analysis. Equally important are advanced model lifecycle (MLOps) processes that enable continuous monitoring, updating, and optimization of models, thereby reducing the risk of model degradation and increasing the reliability of analytical results. In addition to these technological components, organizations need to invest in strengthening employee competencies: finance professionals need to better understand data science and ML principles, while technical teams need to understand financial logic and decision-making processes. Cross-functional collaboration between finance, IT, and data analytics departments is becoming a key factor in effectively aligning technical solutions with business needs and creating real added value. When implemented correctly, the synergy between machine learning and statistics enables companies to make more informed and data-driven decisions, increase the accuracy of forecasts, reduce risk, and ensure higher organizational performance. Such methods are particularly important in operational planning, budgeting, risk assessment, and strategic scenario modeling.
In summary, the integration of ML and traditional statistical models in financial planning is not only a technological innovation but also a strategic step that gives companies a long-term competitive advantage. Hybrid models, combining the strengths of these two methodologies, are becoming an important tool in modern financial analysis practice and help organizations operate in an increasingly complex and volatile environment.