With the rise in internet and e-commerce usage, the significance of credit card usage has also increased. However this surge in credit card usage has unfortunately led to an increase in activities. To address this issue various techniques have been developed. In this research paper we propose a behavior-based classification approach that utilizes vector machines along with a to eliminate affect. By monitoring any deviations in business practice, we can identify activities and further analyze the relevant factors involved. It’s crucial to find a solution to the growing problem of credit card fraud given the amount of data involved. From this perspective, our method specifically leverages the power of support vector machine (SVMs) to understand large data. SVM is known for its ability to stabilize class objects and is particularly suitable for detecting anomalous behavior that may indicate fraud. Our model examines every transaction when integrated into a credit card. This process focuses on patterns and behaviors that differ from the cardholder’s business behavior. SVM provides high accuracy in identifying potential threats by creating a hyperplane that separates legitimate activity from suspicious activity.