COMPUTATIONAL INTELLIGENCE IN FRAUD DETECTION: A REAL-TIME FRAMEWORK FOR ENHANCED CUSTOMER SECURITY

This article presents a comprehensive analysis of computational intelligence implementation in fraud detection systems, focusing on the enhancement of customer security in digital transactions. Through the development and evaluation of an adaptive machine learning framework, we demonstrate how real-time pattern recognition and anomaly detection can significantly improve fraud prevention rates while minimizing false positives. The article examines data from 150,000 digital transactions across multiple sectors, implementing a hybrid approach that combines supervised learning algorithms with dynamic pattern recognition. Results indicate a 94.3% accuracy in fraud detection, with a 76% reduction in response time compared to traditional methods. The proposed framework demonstrates particular effectiveness in identifying subtle fraud patterns through its continuous learning capabilities, adapting to emerging threats while maintaining system efficiency. Furthermore, the article reveals a significant correlation between enhanced computational detection methods and increased customer trust, with survey results indicating a 68% improvement in customer confidence following system implementation. These findings contribute to the growing body of knowledge on automated security measures and provide practical insights for organizations seeking to strengthen their fraud detection capabilities while maintaining operational integrity in an increasingly complex digital landscape.