HARNESSING BIG DATA AND STATISTICAL ANALYTICS TO UNCOVER HIDDEN PATTERNS AND INSIGHTS WITHIN COMPLEX FINANCIAL REPORTING SYSTEMS

This study explores the integration of big data and statistical analytics in financial reporting systems to uncover hidden patterns and enhance financial decision-making. The primary objective is to examine how statistical techniques such as regression analysis, chi-square tests, and time series analysis improve transparency, accuracy, and fraud detection. Using a mixed-methods approach, quantitative data from financial reports (2020-2024) and qualitative insights from industry experts were analyzed. The results indicate a significant correlation (r = 0.87, p < 0.001) between investment in big data analytics and reporting accuracy, with regression analysis (R² = 0.82) showing that a $5M increase in data investments enhances financial accuracy by 3%. The chi-square test (χ² = 45.63, p < 0.001) confirms that improved data integration raises fraud detection rates from 78% to 96%, while time series analysis highlights a 520% surge in financial transactions over the study period. These findings demonstrate the critical role of predictive analytics in streamlining financial reporting. The study concludes that leveraging big data analytics enhances financial integrity, minimizes errors, and strengthens fraud detection. To maximize these benefits, financial institutions should invest in standardized data governance, explainable AI models, and cyber security measures while fostering data literacy through continuous training.