Assessing the Impact of Big Data on the Evolution and Efficacy of Financial Statement Analysis

Abstract

Background: As the number and complexity of financial information increases exponentially, traditional financial statement analysis approaches have limits. This paper investigates the revolutionary effects of Big Data on financial statement analysis, specifically how it reshapes the landscape of financial metric evaluation.  Objective: The article aims to identify the benefits, problems, and future directions that Big Data brings to financial statement examination, so giving a modern roadmap for effective financial scrutiny. Methodology: The study takes a comprehensive approach, integrating various methodologies such as the collection of large financial datasets, the use of machine learning techniques for pattern detection, the examination of case studies for practical insights, and a comparative analysis of traditional techniques and Big Data approaches. Ethical concerns, especially data privacy and security, are thoroughly addressed.  Results: The study's findings show that Big Data has a significant impact on financial prediction accuracy, processing efficiency, and cost-effectiveness in analysis. Crucially, Big Data has been found to improve decision-making quality, strengthen risk management techniques, and refine strategic planning processes in enterprises.  Conclusion: This study demonstrates Big Data's critical significance in the modernization of financial statement analysis. Its conclusions highlight the importance of incorporating Big Data analytics into company financial procedures in order to preserve a competitive advantage. The study also lays the groundwork for future research into the evolution of financial analysis methodologies in the digital age, indicating a significant step forward in the financial sector's adaptability to technological advancement.