An innovative performance analysis for banking transaction using support vector machine algorithm and compare with convolutional neural network.

The objective of this work is to increase the accuracy of credit card fraud prediction using the Support vector machine approach. Materials and Procedures: A G-power value of 89 percent was applied to iterations of a Support Vector Machine and a Convolutional Neural Network (CNN) with 100 samples each, in an innovative method for forecasting the total amount of credit card fraud that will be committed. Support Vector Machines are far more accurate than Convolutional Neural Networks, with a 98.2% accuracy rate (92.4). K-means clustering outperformed Convolutional neural network at the p=0.004 and p=0.005 levels (CNN). In conclusion, the support vector machine approach enables a state-of-the-art decision support system to reliably predict a broader spectrum of financial transactions. [ABSTRACT FROM AUTHOR]