Harnessing Generative Adversarial Networks and AI-Oriented Anomaly Detection Mechanisms for Resilient Fraud and Crisis Mitigation Amidst Pandemic Challenges

Abstract

Background Information: Resilient solutions are required because the COVID-19 pandemic has escalated fraud and system vulnerabilities across industries. In order to reduce fraud and successfully handle crises, this study combines Generative Adversarial Networks (GANs) with AI-driven anomaly detection techniques. We tackle the problems of changing threats, unbalanced data, and instantaneous adaptation in a changing environment. Objectives: In order to improve system resilience against fraud and crises, this project intends to use GANs to generate fraud scenarios, integrate AI for real-time anomaly detection, and create a hybrid framework. Achieving scalability, accuracy, and adaptability for a variety of applications amid pandemic-related challenges is its main goal. Methods: It is suggested to use a hybrid model that combines anomaly detection methods withGANs. While anomaly detection models employ Mahalanobis distance and risk score to detect fake or abnormal data, GANs provide realistic synthetic data to supplement training datasets. Empirical results: With an accuracy rate of 94.5% and an AUC of 0.96, the hybrid model fared better than the separate approaches. When compared to stand-alone methods, the results show enhanced fraud identification, anomaly detection, and cross-domain adaptability. Conclusion: Fraud mitigation and crisis management are improved by combining GANs with AI-driven anomaly detection. In order to create more robust systems in the event of a global crisis, future research should investigate scalability, real-time deployment, and domain-specific applications.