Due to a rapid advancement in the electronic commerce technology, the use of credit cards has increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of credit card fraud also rising. Financial fraud is increasing significantly with the development of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. At this study, we use algorithm of support vector machine for creating the optimized model of SVM to detect the fraudulent online credit card transaction detection. The thesis defines Golden Eagle Optimizer (GEO) as the method of optimization for optimizing both SVM parameters and optimal feature subset at the same time. In this thesis, we use algorithm of support vector machine for creating the optimized model of SVM to detect the fraudulent online credit card transaction detection. The thesis defines GEO as the method of optimization for optimizing both SVM parameters and optimal feature subset at the same time. These data sets include transaction data through credit card emerges from European account holders with 284,807 trades. Similar approaches apply towards both raw including and pre-processed content. The efficiency of the approaches has always been evaluated depending on just the performance assessment dimensions for different classifiers, which will include precision, recall, F1-score, accuracy, and FPR percentage. The experimental results suggest that the proposed algorithm achieves 96.06% higher accuracy on the standard dataset compared to other methods.
