Enhancing Credit and Charge Card Risk Assessment Through Generative AI and Big Data Analytics: A Novel Approach to Fraud Detection and Consumer Spending Patterns
DOI:
https://doi.org/10.48047/6z0p0t08Keywords:
Enhancing Credit and Charge Card Risk Assessment Through Generative AI and Big Data Analytics Keywords: Generative Adversarial Networks, Fraud Detection, Consumer Spending Patterns, Risk Management, Deep Learning, Denoising Differential Privacy, Machine Learning in Finance.Abstract
This study presents a carefully conducted quantitative analysis to demonstrate how generative AI and big data analytics can significantly enhance risk assessment of credit and charge card usage, a key challenge for the banking sector. The underlying data analytics methods can be applied to multiple related data mining challenges, including high-performing fraud detection and pattern analyses to support strategic operational management decision-making through a deep understanding of consumer behavior.
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References
Ravi Kumar Vankayalapati, Dilip Valiki, Venkata Krishna Azith Teja Ganti (2025) ZeroTrust Security Models for Cloud Data Analytics:Enhancing Privacy in Distributed Systems . Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-436. DOI:
doi.org/10.47363/JAICC/2025(4)415
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