Online banking fraud constitutes illegal access to accounts for the payment transfer purposes. There are several reasons for difficulty in the detection of such crimes-from the imbalance of data to the fraudsters’ techniques that are ever-changing. The set of tools used for this purpose are machine learning, economic optimization, and risk assessment. By combining these techniques, a maximum reduction in the losses due to fraud and false positives will be achieved. The machine learning models, when validated against real datasets, were able to reduce losses by 52%, allowing for just 0.4% false positives. The improvement in behavior analysis for fraud detection is conducted through transaction clustering, sliding window aggregation, and adaptive classifiers. The algorithms such as KNN, SVM, Logistic Regression, Local Outlier Factor, and Isolation Forest are used to predict fraud in credit card transactions so that it is accurately detected, false alarms being at a minimum, and customers are availed against unauthorized charging.