It has been the case recently that there has been an escalation in the proposition of untrue activities within different monetary sectors most especially in banks. Consequently, it becomes prudent to adopt strong and resilient strategies intended to minimize loss of finances as well as promote people’s trust. Thus, preventive measures against fraud in the banking sector can be enhanced by utilizing data mining and machine learning techniques which are responsive instruments employed for such purposes. This paper looks at several data mining approaches meant for detecting banking frauds through certain algorithms, case studies and practices. To begin with, this article talks about various data mining techniques whose relevance to fraud detection is very essential. They include classification, anomaly detection, clustering and association rule mining methods. Additionally, it outlines how certain algorithms like logistic regression & decision trees together with k-means clustering & isolation forests help to recognize cheating behaviors in supervised machine learning model. The current information regarding data mining’s application for reducing bank fraud using ML techniques has been compiled in this assessment. It emphasizes how relevant it is for finance professionals to team up with data scientists in order to curb fraud development, identifies important areas that require further research, and proposes ways forward towards improved fraud detection systems.