This table showcases the primary outcomes of the regression analysis for this study. Two models, (1) and (1a), are presented, both using RA as the dependent variable and RGI as a key independent variable. Model (1) uses a standard regression method, while Model (1a) uses bootstrapped estimates for robustness verification. The negative coefficient for RGI indicates a statistical association where higher values of risk governance (RGI) correspond with decreased regulatory adjustments. This association is statistically significant at the 5% level in both models. However, it is crucial to understand that this association does not imply that improving risk governance directly causes a reduction in regulatory adjustments. The relationship merely suggests that the two variables move in opposite directions. The SIZE variable’s positive coefficient suggests that larger banks tend to have increased regulatory adjustments. This finding is significant at the 5% level in Model 1 and the 10% level in Model 1a. Other variables, such as CEOAD and BS, do not show statistically significant coefficients, indicating their potential limited impact on regulatory adjustments. The models account for bank and year-fixed effects, controlling for unobserved bank-specific attributes and common time-related effects. Robust standard errors, clustered by bank, are used to mitigate potential issues with heteroskedasticity and autocorrelation. The models' adjusted R-squared value of 0.8951 indicates that the included variables account for approximately 89.51% of the variability in regulatory adjustments. The high adjusted R-squared value in the regression models is influenced by the inclusion of the RGI variable, derived from COMP1 of the PCA analysis. COMP1 captures a significant portion of the variance from the original data set, contributing to the model’s explanatory power. However, the overall model specification and other variables also play a role in achieving this high R-squared value. Robust standard errors in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1