This study examines gender-based differences in financial behavior, focusing on various bank transactions and demographic factors using a large dataset from Kaggle, consisting of over one million entries. By applying multiple linear regression models and permutation feature importance rankings, the study explores how different variables, such as age, geographic location, and transactional timing, influence banking patterns across genders. The findings reveal that age is a significant predictor of transaction behavior for males, while it is less impactful for females. Geographic location, particularly “Location_West” and “Location_Other,” plays a crucial role for males but has minimal influence on females. For females, transactional timing, indicated by “TransactionHour,” shows more importance in predicting banking behaviors. In contrast, gender itself does not significantly affect transaction outcomes when controlling for other variables. Overall, the study highlights the importance of demographic and contextual factors, such as age and geography, over inherent genderbased differences. These insights provide valuable guidance for financial institutions aiming to tailor their services more effectively to meet the needs of diverse customer segments. The results emphasize the need for data-driven approaches to better understand gender-specific financial behavior and improve service offerings.