Article’s

The rapid growth of digital payment systems has significantly increased the risk of fraudulent transactions, posing serious challenges to financial institutions and users. Detecting fraud accurately and efficiently has become essential to maintain the security and reliability of online payment platforms. Traditional rule-based systems often fail to identify complex and evolving fraud patterns, creating the need for intelligent and adaptive approaches. This project presents a fraud detection system in payment environments using Genetic Algorithms (GA). Genetic Algorithms, inspired by the principles of natural evolution, are used to optimize feature selection and improve the performance of classification models. By selecting the most relevant features from large datasets, GA enhances detection accuracy while reducing computational complexity. The proposed system integrates Genetic Algorithms with machine learning techniques to identify fraudulent transactions. The methodology involves data preprocessing, feature optimization using GA, and classification using suitable models. The performance of the system is evaluated using metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that the use of Genetic Algorithms significantly improves fraud detection efficiency by reducing false positives and increasing the detection rate of fraudulent activities. This approach provides a scalable and effective solution for modern payment systems, making it highly suitable for real-time fraud detection applications.