Automation of Financing Processes and Its Impact on Financial Performance in the Kingdom of Saudi Arabia After 2020: A Theoretical and Analytical Review in Light of the Objectives of Saudi Vision 2030

Abstract

This study examined the relationship between financing process automation and financial performance in financial institutions, with particular emphasis on the Saudi context and the objectives of Saudi Vision 2030. It adopted an integrative theoretical and analytical review of studies published between 2021 and mid-2026. The search was conducted through Google Scholar, Crossref, publishers’ and journals’ websites, the Algerian Scientific Journals Platform (ASJP), and the Egyptian Knowledge Bank (EKB). Twelve studies met the inclusion criteria, comprising five Arabic and seven international studies. The studies were compared in terms of geographical context, research design, sample or data source, technology measurement, performance indicators, and key findings.
The findings varied according to research design and measurement method. Survey-based studies identified positive relationships in some areas related to service quality, customer satisfaction, perceived performance, and operational efficiency, but they did not provide evidence of actual improvement in accounting profitability. Studies based on financial data produced mixed findings. Some reported significant relationships with selected performance indicators, while others found no statistically significant relationship with return on assets or return on equity. Evidence directly related to automation was clearer regarding reduced processing time, waste, errors, and operating costs than regarding profitability. The review also revealed that most studies examined FinTech or digital transformation broadly rather than directly measuring automation across the financing cycle. The study concludes that the available evidence does not establish a direct causal relationship between financing process automation and financial performance. It recommends developing precise measures of automation maturity and conducting longitudinal Saudi studies linking automation with financial, operational, portfolio-quality, and risk indicators.