An Intelligent Framework for Prioritizing Financial Accounting Strategies in Electronic Banking Based on Computational Intelligence

Abstract

The purpose of this study is to develop an intelligent and transparent framework for prioritizing financial accounting strategies within the context of electronic banking, in a manner that simultaneously leverages high computational capacity while addressing the essential requirements of transparency, auditability, and regulatory compliance in financial accounting systems. In terms of purpose, the research is applied–developmental, and in terms of methodology, it adopts a mixed-methods approach (qualitative–quantitative). The study was conducted in three phases. In the qualitative phase, novel financial accounting indicators relevant to electronic banking were identified and screened using content analysis and the fuzzy Delphi technique. In the quantitative phase, the interrelationships among criteria were modeled and weighted using the Analytic Network Process (ANP), in order to account for nonlinear dependencies between accounting and technical criteria. In the decision-making phase, strategic alternatives were prioritized through the application of the TOPSIS and SAW methods, and the algorithms were implemented in the Python environment to enhance computational transparency and reproducibility. The findings indicate that criteria such as reporting transparency, data governance, cybersecurity, and real-time auditability carry the greatest weight in the selection of financial accounting strategies. The results of the TOPSIS model show that strategies such as continuous auditing and blockchain-based accounting systems, despite their higher cost and complexity, achieve the highest priority due to their superiority in transparency and trustworthiness. The convergence of results obtained from the TOPSIS and SAW methods confirms the robustness and validity of the proposed model. Overall, the study demonstrates that financial accounting in electronic banking has evolved from a mere event-recording system into an intelligent data governance ecosystem. By integrating computational intelligence with accounting principles, the proposed framework substantially bridges the gap between accounting theory and the practical requirements of digital banking. The results of this research can serve as a reliable foundation for financial managers, auditors, and banking policymakers in the design and selection of modern accounting strategies.