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What is GANs for Credit Risk in Supply Chains about?

This paper explores the use of Generative Adversarial Networks (GANs) to enhance credit risk identification in supply chains, addressing challenges related to data scarcity and imbalanced datasets. The study demonstrates that GANs can generate synthetic credit risk scenarios, improving predictive accuracy and enabling better risk management across various industries. Experimental results indicate that the GAN-based model outperforms traditional methods, highlighting its potential for proactive financial dis

Author

pmaya5279

Language

EN