Identifying Key Components in Optimizing Financial Processes Using Computational Intelligence

Abstract

The objective of this study is to identify key components in optimizing financial processes using computational intelligence and to examine its role in improving financial systems. This qualitative study was conducted using qualitative content analysis. Data were collected through semi-structured interviews with 26 financial managers and specialists from companies based in Tehran. Participants were selected purposefully, and sampling continued until theoretical saturation was reached. The collected data were analyzed using NVivo software through open, axial, and selective coding methods. The findings revealed that optimizing financial processes based on computational intelligence can be explained in four main dimensions: (1) organizational factors, including data-driven culture, organizational agility, and management support; (2) computational intelligence technologies, including machine learning algorithms, big data processing, and process automation; (3) implementation challenges, including employee resistance to technological changes, lack of technological infrastructure, and data quality issues; and (4) optimization outcomes, such as increased accuracy of financial analysis, reduced operational costs, and improved financial reporting transparency. The study concludes that using computational intelligence in financial processes can enhance financial decision-making, improve efficiency, and promote transparency in organizations. However, challenges such as infrastructure weaknesses and organizational resistance must be addressed. This research highlights the importance of a data-driven culture and employee training in adopting new technologies.