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Synthesizing Individual Consumers′ Credit Historical Data Using Generative Adversarial Networks

Department of Computer Science, Sejong University, Seoul 05006, Korea
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Author to whom correspondence should be addressed.
These authors contributed equally.
Appl. Sci. 2021, 11(3), 1126; https://doi.org/10.3390/app11031126
Received: 28 December 2020 / Revised: 20 January 2021 / Accepted: 22 January 2021 / Published: 26 January 2021
(This article belongs to the Section Computing and Artificial Intelligence)
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. Financial data has never been more valuable, especially when assessed jointly with data from different industries, including healthcare, insurance, credit bureau, and research institutions. Therefore, it is critical to generate synthetic datasets that retain the statistical or latent properties of the real datasets as well as the privacy protection guaranteed. In this paper, we apply Generative Adversarial Nets (GANs) to generating synthetic consumer credit data to be used for various educational purposes, specifically in developing machine learning models. GAN is preferable to other pseudonymization methods such as masking, swapping, shuffling, or perturbation, for it does not suffer from adding more attributes or data. This study is significant because it is the first attempt to generate the synthetic data of real-world credit data in practical use. The results find that synthetic consumer credit data using GAN shows a substantial utility without severely compromising privacy and would be a useful resource for big data training programs. View Full-Text
Keywords: consumer credit historical data; synthetic data generation; generative adversarial networks; artificial intelligence data mining; financial big data consumer credit historical data; synthetic data generation; generative adversarial networks; artificial intelligence data mining; financial big data
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MDPI and ACS Style

Park, N.; Gu, Y.H.; Yoo, S.J. Synthesizing Individual Consumers′ Credit Historical Data Using Generative Adversarial Networks. Appl. Sci. 2021, 11, 1126. https://doi.org/10.3390/app11031126

AMA Style

Park N, Gu YH, Yoo SJ. Synthesizing Individual Consumers′ Credit Historical Data Using Generative Adversarial Networks. Applied Sciences. 2021; 11(3):1126. https://doi.org/10.3390/app11031126

Chicago/Turabian Style

Park, Nari; Gu, Yeong H.; Yoo, Seong J. 2021. "Synthesizing Individual Consumers′ Credit Historical Data Using Generative Adversarial Networks" Appl. Sci. 11, no. 3: 1126. https://doi.org/10.3390/app11031126

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