Provide a new approach to identify and detect credit card fraud using ANN - ICA

Colonial Competition Algorithm is a new algorithm in the field of intelligent optimization. In this paper, the optimization algorithm is used to learn a neural network. The neural network used to solve the problem of classifying bank data. The results of the application of the colonial competition algorithm to the problem of learning a classifier neural network and its comparison with the genetic algorithm indicate the high success of the proposed method. Transactions and fraud occur in the course of exchanges. Therefore, detecting fraud is one of the most important issues in financial institutions and banks. With the growth of credit card transactions as one of the branches of electronic banking in the payment system, fraud in these cards has also increased. Despite much research on fraud detection in this particular area, fraudsters have also developed fraudulent methods to prevent detection. Recent methods include tracking transactions through address authentication systems, card authentication methods, personal identification numbers, and biometric methods. In this project, credit card fraud detection with the aim of high detection rate and accuracy and the lowest system error rate using neural networks and its combination with colonial competition algorithm and also extracting effective features to evaluate fraud detection is another goal of this research.