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.