Designing a Data-Driven Governance Model in Corporate Financial Management with Emphasis on Transparency, Accountability, and Algorithmic Decision-Making
Keywords:
Data-driven governance, financial management, , financial transparency, algorithmic accountability, algorithmic decision-making, internal control, data analyticsAbstract
This study aimed to design a data-driven governance model for corporate financial management with an emphasis on transparency, accountability, and algorithmic decision-making. This qualitative study was conducted using a thematic analysis design. The participants consisted of financial experts, chief financial officers, internal auditors, risk managers, financial information technology specialists, and data analysts working in companies located in Tehran. Participants were selected through purposive and snowball sampling, and data collection continued until theoretical saturation was reached. A total of 18 participants took part in the study. Data were collected through semi-structured interviews. After obtaining informed consent, the interviews were recorded, transcribed verbatim, and analyzed using NVivo software. Data analysis was performed through open, axial, and selective coding, with the aim of identifying main and subthemes related to data-driven financial governance. The analysis led to the identification of four main categories: institutional architecture of financial data governance, transparency and traceability of data and algorithmic flows, accountability and auditability of algorithmic decision-making, and organizational capacity-building for data-driven financial management. The findings indicated that data-driven governance in financial management is not merely a technological issue; rather, it is an institutional, control-oriented, and cultural mechanism that should integrate data decision rights, data quality, data ownership, documentation of analytical models, human oversight, internal controls, algorithmic risk management, and financial-data literacy. The proposed model suggests that data-driven governance in corporate financial management becomes effective when data transparency, managerial accountability, and control over algorithmic decision-making are simultaneously embedded in corporate governance structures, internal control systems, financial reporting processes, and information technology architecture.
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