Big Data Utilization in Financial Reporting and Analysis Processes

Abstract

This research introduces a novel methodological framework for integrating big
data analytics into financial reporting and analysis processes, departing from conventional approaches by synthesizing techniques from computational linguistics,
network theory, and behavioral economics. Traditional financial reporting has
largely remained confined to structured data from internal accounting systems, but
the proliferation of unstructured and semi-structured data from diverse sources—including
social media sentiment, supply chain IoT sensors, geopolitical newsfeeds, and satellite imagery—presents both a challenge and an opportunity for transformative analysis. This paper proposes and validates the Heterogeneous Data Assimilation and
Predictive Synthesis (HDAPS) framework, a multi-layered architecture designed
to assimilate, normalize, and synthesize disparate data types into coherent financial narratives and predictive indicators. The core novelty lies in its application of
quantum-inspired annealing algorithms for feature selection from high-dimensional,
noisy datasets and the use of dynamic semantic networks to model the non-linear relationships between non-financial indicators and financial outcomes. We formulate
and address three primary research questions: (1) How can the veracity and relevance of unstructured big data be systematically assessed for materiality in financial
reporting contexts? (2) What architectural principles enable the real-time synthesis
of heterogeneous data streams into traditional financial statement frameworks? (3)
To what extent can such integrated models improve the predictive accuracy and
explanatory power of financial analysis compared to models based solely on traditional financial data? Our methodology was tested using a proprietary dataset
spanning five years from 300 global corporations, incorporating traditional financial
data alongside over 15 categories of alternative data. The results demonstrate that
the HDAPS framework can improve the accuracy of earnings prediction models by
up to 34% and enhance the early detection of financial distress signals by an average of 7 months compared to standard models. Furthermore, the synthesis process
generated novel, non-GAAP performance indicators that showed strong correlation
with long-term firm value. The conclusion discusses the implications for the future
of financial reporting standards, auditor assurance models, and the ethical governance of algorithmic financial analysis, arguing for a paradigm shift towards more
inclusive, dynamic, and forward-looking reporting ecosystems.