Vulnerability in banking systems has exposed us to fraudulent acts, which cause severe damage to both customers and the bank in terms of loss of money and reputation. Financial fraud in banks is estimated to result in a significant amount of financial loss annually. Early detection of this helps to mitigate the fraud, by developing a counter strategy and recovering from such losses. A machine learning-based approach is proposed in this paper to contribute to fraud detection successfully. Users of internet banking have separate accounts, which are overseen by banks or physical stores. The purpose of this work is to use the Logistic Algorithm and MultiNominal Niave Bais algorithms to identify and stop online banking fraud. Simultaneously, we have attempted to guarantee that legitimate transactions are not declined by employing a one-time password produced by the bank server and transmitted to the specific consumers by SMS to their system-registered mobile number.