Synthesizing Individual Consumers′ Credit Historical Data Using Generative Adversarial Networks

Synthesizing Individual Consumers′ Credit Historical Data Using Generative Adversarial Networks

The financial sector accumulates a massive amount of consumer data that contain the most sensitive information daily. These data are strictly limited outside the financial institutions, sometimes even within the same organization, for various reasons such as privacy laws or asset management policy....

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Bibliographic Details
Main Authors: Nari Park, Yeong Hyeon Gu, Seong Joon Yoo
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Applied Sciences
Subjects:
consumer credit historical data
synthetic data generation
generative adversarial networks
artificial intelligence data mining
financial big data
Online Access:https://www.mdpi.com/2076-3417/11/3/1126

Internet

https://www.mdpi.com/2076-3417/11/3/1126

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