From ESG to Financial Stability: Unpacking the Multi-Dimensional Impact of AI-Driven FinTech-Related Technology Adoption on Bank Performance

This study examines the association between Saudi banks’ internal adoption of AI-enabled FinTech-related digital tools and their financial performance, sustainability performance, and financial stability over the period 2015–2024. Using a panel dataset of 10 banks, the analysis investigates how the adoption of AI-driven technologies—such as machine-learning credit assessment, robo-advisory systems, and automated compliance tools—is related to market performance (Tobin’s Q), accounting performance (ROA and ROE), financial stability (Z-Score), and sustainability outcomes measured by both Bloomberg ESG Disclosure Score and the LSEG ESG performance-oriented score. To ensure robust inference and reduce simultaneity concerns, the empirical strategy employs Pooled OLS and Fixed Effects Models with Driscoll–Kraay standard errors, as well as a dynamic Fixed Effects Models incorporating lagged dependent variables, lagged independent variables, and shock-interaction terms. Bank-specific characteristics—including size, age, leverage, liquidity, loan-to-deposit ratio, non-performing loans, net interest margin, market capitalization, and board size—are included as controls. The findings indicate a positive and statistically significant relationship between banks’ internal adoption of AI-enabled digital/FinTech-related technologies and their financial performance, sustainability performance, and financial stability. These relationships remain robust across estimation approaches, providing insights for policymakers, regulators, and bank managers seeking to advance digital transformation while safeguarding financial soundness and supporting sustainable development in the Saudi banking sector.