Year: 2025
Type: article
Abstract: The article explores how generative artificial intelligence transforms credit card fraud detection, addressingpersistent challenges in the financial industry. It introduces the Generative AI Fraud Detection Framework(GAI-FDF), which integrates adversarial machine learning, synthetic data generation, and adaptive learningcapabilities to overcome limitations of traditional approaches. The framework enables financial institutionsto proactively simulate fraudulent behaviors, generate synthetic transaction patterns to address data scarcityissues, and implement self-learning models that continuously adapt to emerging threats. Case studies frommajor financial institutions demonstrate significant improvements in reducing false positives whileincreasing detection accuracy across various fraud types. The article examines implementation strategies,technical components, and organizational considerations necessary for successful deployment, whileproviding recommendations for security leaders, fraud prevention teams, and model auditors navigatingthis evolving landscape.
Authors: Rahul Vats -, Srinivasa Sunil Chippada -
Institution: University of Arizona
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409218747