As technology developed, new business-making mechanisms emerged in the financial sector. One of them is the credit card system. But due to several flaws in this method, numerous issues are raised in this system by credit card frauds. The industry as well as customers who use credit cards is suffering greatly as a result. Lessons on investigating actual credit card figures in relation to privacy concerns are lacking. In the publication, an effort has been made to uncover credit card fraud using algorithms that used machine learning approaches. In this regard, two algorithms are used via Fraud Detection in credit card using Decision Tree and Fraud Detection using Random Forest. Some available online data can be used as a sample to determine the model's efficacy. Then, a financial institution's genuine globe credit card details group is analyzed . Additionally, additional noise is added to the data samples in order to auxiliary assess the systems' durability. The first approach in the study is significant since it builds a tree against the user's behaviors, and by utilizing this tree, frauds will be detected. In the second way, a user activity-based forest will be built, and it will be attempted to identify the suspect using this forest. The findings of the analysis unequivocally demonstrate that the common elective method detects credit card fraud situations with respectable degrees of precision.