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

SRI HARSHA GRANDHI; SUNDARAPANDIAN MURUGESAN; RAJYA LAKSHMI GUDIVAKA; RAJ KUMAR GUDIVAKA; BASAVA RAMANJANEYULU GUDIVAKA; DINESH KUMAR REDDY BASANI

doi:10.46243/jst.2022.v7.i04pp221-234

Authors

SRI HARSHA GRANDHI
SUNDARAPANDIAN MURUGESAN
RAJYA LAKSHMI GUDIVAKA
RAJ KUMAR GUDIVAKA
BASAVA RAMANJANEYULU GUDIVAKA
DINESH KUMAR REDDY BASANI

DOI:

https://doi.org/10.46243/jst.2022.v7.i04pp221-234

Keywords:

GANs, anomaly detection,, fraud mitigation,, crisis management, pandemics.

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.

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