Credit card fraud commonly happens in financial institutes such as banks. Fraud results in a huge financial damage that may reach to billions of dollars every year. Detecting and preventing credit card fraud manually is a labor intensive and relatively ineffective approach. Therefore, a significant effort was made to develop automated solutions for fraud detection. Researchers dedicated their works on designing and developing models and systems in particular, the fraud anlaysis systems that enable to detect different types of fraud in different sectors including insurance, telecommunication, financial audit, financial markets, money laundering, credit card, etc. However, some problems remains unsolved. Of all, the most prevalent one is the extreme class imbalance. In this paper, we aimed at addressing this problem. We focused on the K-Nearest Neighbor (KNN) classifier and investigated the cost-sensitive approaches used for KNN. Also, we presented a novel cost-sensitive KNN approach that we developed using Cosine Similarity (CoS). We compared our model with the other methods to verify its efficiency, and we proved using several performance measures that it's a better approach than other KNN algorithms.