International Journal on Science and Technology

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Leveraging Generative AI for Fraud Detection in Credit Card Transactions

Author(s) Rahul Vats, Srinivasa Sunil Chippada
Country United States
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.
Keywords Generative adversarial networks, synthetic data augmentation, anomaly detection, self-learning models, cross-bank intelligence sharing
Field Computer Applications
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-04-04
Cite This Leveraging Generative AI for Fraud Detection in Credit Card Transactions - Rahul Vats, Srinivasa Sunil Chippada - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.3107
DOI https://doi.org/10.71097/IJSAT.v16.i2.3107
Short DOI https://doi.org/g9dpfs
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