Navigating the Computational Turn: Global Trends and Cross-Cultural Challenges in AI-Enabled Financial Statement Analysis
Abstract
Objective
The paper aims to map the recent methodological reconfiguration of financial statement analysis (FSA) and to identify the analytical trajectories emerging between 2015 and 2025. It specifically examines how corporate disclosure is being reshaped by the computational turn, as it expands beyond structured financial data to include narrative, sustainability and machine-readable reporting across different cultural and regulatory contexts.
Methodology
A Scopus-based bibliometric design was adopted, applied to a corpus of 454 documents. It combines performance analysis, co-word analysis, thematic mapping and trend-topic analysis, processed through the Bibliometrix/ Biblioshiny environment to visualize the evolution of the field.
Findings
The results indicate that FSA is undergoing a profound rearticulation, moving from traditional ratio-based interpretation to an AI-enabled ecosystem. The evidence suggests a three-phase evolution: an initial decision-support phase, an intermediate expansion towards broader reporting architectures and non--financial disclosures and a recent computational turn centred on AI-enabled methods. The analysis reveals a five-cluster conceptual structure: (1) the traditional financial-statement core, (2) computational learning methods (ML/DL), (3) predictive applications in auditing and fraud detection, (4) sustainability and ESG extensions and (5) the cross-cultural dimensions of accounting research. Furthermore, the findings highlight a recent shift in which Artificial Intelligence (AI), natural language processing (NLP) and Large Language Models (LLMs) have become foundational to navigating global disclosure processing costs. Finally, the geography of collaboration reveals that methodological innovation in FSA is being shaped through specific cross-cultural research corridors, particularly the Sino–Anglophone and Asia–Pacific networks, while European participation currently lags behind regulatory developments such as CSRD/ESRS.
Value Added
The study contributes to the literature by conceptualizing the “computational turn” in FSA, offering an empirically grounded map of field’s methodological evolution. The paper argues that this transformation is a rearticulation rather than a substitution; new computational methods are being embedded within the traditional inferential logic of FSA rather than replacing it. Furthermore, the research integrates previously isolated streams – specifically NLP in accounting, machine learning in finance, ESG analytics and digital reporting – into a unified framework. A key contribution lies in the finding that while AI provides universal analytical power, cross-cultural challenges in the linguistic and contextual interpretation of narrative disclosures remain a critical frontier. By repositioning FSA as a domain where language, accounting tradition and institutional culture jointly dictate the credibility of computational measurements, this study opens a vital research interface between international management and global accounting practices.
Recommendations
Future research should develop integrative cross-stream studies that treat the five identified clusters as connected subsystems, with particular attention paid to the interface between computational methods and sustainability reporting. Domain-specific validation frameworks for ML applications in accounting and structured research programmes on LLM-based financial document analysis have been identified as priority agendas.
© 2026 Justyna Fijałkowska, Federico Lanzalonga, published by SAN University
This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 License.