NDLI: Synthesizing Individual Consumers′ Credit Historical Data Using Generative Adversarial Networks

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Synthesizing Individual Consumers′ Credit Historical Data Using Generative Adversarial Networks

Content Provider	MDPI
Author	Park, Nari Gu, Yeong Hyeon Yoo, Seong Joon
Copyright Year	2021
Description	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.
Starting Page	1126
e-ISSN	20763417
DOI	10.3390/app11031126
Journal	Applied Sciences
Issue Number	3
Volume Number	11
Language	English
Publisher	MDPI
Publisher Date	2021-01-26
Access Restriction	Open
Subject Keyword	Applied Sciences Information and Library Science Consumer Credit Historical Data Synthetic Data Generation Generative Adversarial Networks Artificial Intelligence Data Mining Financial Big Data
Content Type	Text
Resource Type	Article

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