Digital banking expansion created increased exposure of financial systems to fraudulent activities which threatens institutions and their customers with considerable risks. The conventional rule-based fraud detection systems display difficulties when they need to evolve alongside changing fraud patterns while producing numerous false alert reports. Researchers examine how machine learning algorithms detect bank system frauds in their pursuit to develop more precise and versatile solution methods. The researchers used transaction data and carried out preprocessing steps which included class balancing together with normalization protocols to clean up the dataset. The supervised learning algorithm comprised Decision Tree and Support Vector Machine (SVM) and Random Forest for modeling as well as performance results assessment. The Random Forest algorithm showed exceptional performance because it discovered the most fraudulent transactions with its highest accuracy and recall level. The research establishes machine learning as an efficient big data solution for real-time fraud prevention which provides strong capabilities to fight financial crimes in digital banking networks.