Credit card fraud has played a major issue for both cardholders and the issuing authorities as a notable financial challenge. To address this issue here involves modified Convolutional Neural Network (CNN) to identify fraudulent activities. The proposed methodology involves data pre processing, data visualization, validation and evaluation. Here the data is preprocessed by normalization to improve the quality of data performance and training stability of deep neural network. One hot encoding is used to convert categorical variables into binary format and is highly essential for data processing which in turn improve the prediction and classification accuracy of the method. Furthermore by using CNN with a modified Quantum Vortex Search Algorithm (MQVSA) is developed to increase the accuracy and effectiveness of the credit card fraud detection. The MQVSA improves CNN capacity of classification and to identify complex pattern indicative of fraud and model parameters. Thus the proposed method used in this work is performed in the python software and thus the classifier achieve the remarkable precision of 98% and the sensitivity value of 97% and effectively predicting the credit card fraud by evaluation and validation.