A Qualitative Analysis of Big Data and Artificial Intelligence Adoption in Enhancing Financial Transparency
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Abstract
The integration of digital technologies is reshaping accounting and finance, with Big Data and Artificial Intelligence (AI) playing an increasingly vital role in strengthening financial transparency. This study employs a qualitative research design to explore how financial institutions and related businesses perceive and implement Big Data and AI in their financial reporting practices. Semi-structured interviews were conducted with managers, auditors, IT specialists, and regulators to capture diverse perspectives. The findings reveal four major themes: (1) Big Data and AI adoption enhances accuracy, timeliness, and fraud detection in financial disclosures; (2) significant challenges persist, including high implementation costs, lack of expertise, and resistance to change; (3) ethical and regulatory concerns, particularly algorithmic bias, accountability, and data privacy, remain central to the adoption process; and (4) transparency improvements contribute to building stakeholder trust, though trust depends on assurances of responsible data use and auditable systems. This study contributes to the literature by offering qualitative insights beyond quantitative performance metrics, highlighting the cultural, ethical, and organizational dimensions of technological adoption. The results suggest that achieving financial transparency through digital tools requires not only technological investment but also governance, training, and regulatory alignment. The paper concludes with implications for theory, practice, and policy, along with recommendations for future research directions.
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