Using Data Science Methodologies to Evaluate the Effectiveness of Financial Decisions

Анотація

Modern financial systems face challenges associated with increasing data volumes, the complexity of decision-making processes, and the need to adapt to rapidly changing market conditions. The relevance of applying Data Science methodologies for assessing financial decisions has been substantiated. It has been determined that these methods enable the analysis of large datasets, enhance forecasting accuracy, automate processes, and minimize risks. However, it has been identified that implementing Data Science in the financial sector faces issues such as limited access to high-quality data, a shortage of technical expertise, and ethical risks, necessitating targeted solutions. 

The study aims to justify the effectiveness of using Data Science methodologies for assessing financial decisions, which will improve analytical accuracy, optimize decision-making processes, and enhance financial resource management. System analysis methods have been employed to identify challenges in integrating Data Science methodologies, modeling has been applied to evaluate their impact on financial decision-making efficiency, and a comparative analysis of existing algorithms and models has been conducted. 

The research results confirm that employing Data Science methods enables financial institutions to achieve significant advantages in decision-making. It has been established that classification algorithms, predictive analysis, and neural networks contribute to improving assessment accuracy, risk minimization, and process automation. Hybrid models, combining traditional statistical methods with machine learning, provide a deeper analysis of financial data and facilitate risk reduction. 

The conclusions emphasize the necessity of developing a robust infrastructure for implementing Data Science methodologies, training specialists, and establishing ethical standards for algorithm use. Practical recommendations include creating automated data collection systems and introducing transparent mechanisms for interpreting analysis results. 

Future research perspectives involve developing adaptive machine learning models capable of functioning in unstable market conditions and exploring the impact of ethical considerations on Data Science implementation in financial analytics. Particular attention will be paid to expanding the capabilities of these technologies for portfolio management and risk forecasting.