Big Data Analytics in Financial Statement Analysis: A Systematic Review of Challenges, Techniques, and Future Directions

Abstract

The integration of big data analytics has significantly transformed financial statement analysis, enhancing the accuracy and efficiency of financial reporting. Traditional financial analysis methods often rely on structured data and manual interpretation, which can be time-consuming and prone to errors. However, the increasing complexity and volume of financial data demand more advanced analytical approaches to improve decision-making and transparency. As a result, big data analytics has emerged as a powerful tool that utilizes machine learning, predictive modeling, and artificial intelligence to extract meaningful insights from large datasets. Despite its benefits, several challenges hinder the effective implementation of big data analytics in financial statement analysis. These include data integration issues, cybersecurity threats, regulatory compliance complexities, and a lack of expertise in handling big data tools. Furthermore, financial professionals often struggle with interpreting unstructured data sources, such as textual information from financial disclosures and market sentiment. To address these challenges, this review paper examines the role of big data analytics in financial statement analysis, highlighting its methodologies, benefits, and limitations. The study explores various analytical techniques, including predictive analytics, anomaly detection, and sentiment analysis, to improve financial reporting accuracy. Additionally, it discusses future directions for developing automated analytical frameworks and regulatory adaptations that enhance data reliability and security. This paper provides a comprehensive review of existing research, offering valuable insights into how big data analytics is reshaping financial statement analysis and the potential solutions to overcome current challenges.