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

Subject Areas : Multimedia Processing, Communications Systems, Intelligent Systems

Javad Balaee kodehi ¹ (MSc. Student, Faculty of Electrical and Computer Engineering, Zanjan Branch, Islamic Azad Universitty, Zanjan, Iran.)
Mohammad Tahghighi Sharabyan ² (Assistant Professor, Faculty of Electrical and Computer Engineering, Zanjan Branch, Islamic Azad University, Zanjan,Iran)

Received: 2021-08-09 Accepted : 2021-09-23 Published : 2022-06-22

Keywords: credit card, neural network combination, fraud detection, Colonial Competition, algorithm,

Abstract :

Introduction: The imperialistic competition algorithm is a method in the field of evolutionary computing that deals with finding the optimal answer to various optimization problems. This algorithm provides an algorithm for solving mathematical optimization problems by mathematical modeling the socio-political evolution process. The imperialistic competition algorithm forms an initial set of possible answers. These initial answers are known as chromosomes in the genetic algorithm, particles in the particle swarm algorithm, and countries in the imperialistic competition algorithm. The imperialistic competition algorithm gradually improves these initial solutions (countries) with a special process that follows and finally provides the appropriate solution to the optimization problem. By imitating the process of the social, economic, and political evolution of countries and by mathematically modeling parts of this process, this algorithm presents operators in a regular form as an algorithm that can help solve complex optimization problems. In fact, this algorithm looks at the solutions of the optimization problem in the form of countries and tries to gradually improve these solutions during an iterative process and finally reach the optimal solution of the problem.Method: The proposed algorithm of this article (combined algorithm of neural network and colonial competition) has used the social-political process of the imperialistic competition algorithm with mathematical modeling in order to provide a strong and efficient algorithm in the field of diagnosis optimization.Findings: Our experiments proved that neural data classification using the transaction rejection option can lead us to a very low error rate, while we are looking for a very high detection rate. In this study, we reached an accuracy rate of 98.54, which is a higher accuracy rate compared to previous methods.Discussion: In this research, credit card fraud detection has been done with the aim of identifying the fraud rate, increasing the accuracy, and applying the lowest system error rate using neural networks and combining it with the colonial competition algorithm. Also, effective features were extracted in the evaluation of fraud detection. It can be concluded that the proposed classification system can have a very high detection performance in credit card financial transactions.

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