The study explores the escalating occurrences of financial misconduct within banking institutions resulting from the expansion of services, which leads to substantial financial damages experienced by both the banks and their clients. The primary aim is to meticulously analyze transactional data and user conduct to promptly pinpoint any suspicious activities. A vast dataset of over 300,000 records was utilized to determine the perfect algorithm based on a variety of factors. The refinement of features is carried out to enhance the accuracy of predictions, data normalization is implemented, and any inaccuracies are rectified. The SN algorithm emerges as the most efficient approach for uncovering fraudulent behaviors, outperforming alternative machine learning methodologies. This research underscores the imperative nature of utilizing advanced machine learning methods in the development of resilient fraud detection systems within the constantly evolving financial sector.