Authenticating a user in the right way is essential to IT systems, where the risks are becoming more and more complex. Especially in the mobile world, banking applications are among the most delicate systems requiring strict rules and regulations. Existing approaches often require point-of-entry authentication accompanied by a one-time password as a second-factor authentication. However, this requires active participation of the user and there is continuous authentication during a session. In this paper, we investigate whether it is possible to continuously authenticate users via behavioral biometrics with a certain performance on a mobile banking application. A currently used mobile banking application in Turkey is chosen as the case, and we developed a continuous authentication scheme, named DAKOTA, on top of this application. The DAKOTA system records data from the touch screen and the motion sensors on the phone to monitor and model the user's behavioral patterns. Forty-five participants completed the predefined banking transactions. This data is used to train seven different classification algorithms. The results reveal that binary-SVM with RBF kernel reaches the lowest error scores, 3.5% equal error rate (EER). Using the end-to-end DAKOTA system, we investigate the performance in real-time, both in terms of authentication accuracy and resource usage. We show that it does not bring extra overhead in terms of power and memory usage compared to the original banking application and we can achieve a 90% true positive recognition rate, on average.